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                    <h1 class="text-lg md:text-xl font-bold text-gray-800">arXiv 每日论文精选</h1>
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                        <i class="fa fa-calendar-o mr-1"></i>2025-11-05
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                    <span class="text-gray-500 mr-1"><i class="fa fa-file-text-o"></i> 总论文数:</span>
                    <span id="total-papers" class="font-semibold text-primary">104</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-star"></i> 精选论文数:</span>
                    <span id="selected-papers" class="font-semibold text-accent">16</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-line-chart"></i> 平均评分:</span>
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                <span id="display-count" class="font-medium">显示 104 篇论文 (共 104 篇)</span>
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02181v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>KGBridge：面向非重叠跨领域推荐的知识引导提示学习
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            KGBridge: Knowledge-Guided Prompt Learning for Non-overlapping Cross-Domain Recommendation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuhan Wang, Qing Xie, Zhifeng Bao, Mengzi Tang, Lin Li, Yongjian Liu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究非重叠用户场景下的跨域推荐问题，核心思想是通过知识图谱增强的提示编码器和两阶段训练范式，将关系级语义建模为软提示，实现领域共享与领域特定知识的显式解耦和平衡迁移。</p>
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对非重叠用户的跨域推荐核心挑战，提出知识引导的提示学习框架，与推荐系统核心领域进展和直接LLM应用高度相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 01:50:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02181v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02181v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Knowledge Graphs (KGs), as structured knowledge bases that organize relational information across diverse domains, provide a unified semantic foundation for cross-domain recommendation (CDR). By integrating symbolic knowledge with user-item interactions, KGs enrich semantic representations, support reasoning, and enhance model interpretability. Despite this potential, existing KG-based methods still face major challenges in CDR, particularly under non-overlapping user scenarios. These challenges arise from: (C1) sensitivity to KG sparsity and popularity bias, (C2) dependence on overlapping users for domain alignment and (C3) lack of explicit disentanglement between transferable and domain-specific knowledge, which limit effective and stable knowledge transfer. To this end, we propose KGBridge, a knowledge-guided prompt learning framework for cross-domain sequential recommendation under non-overlapping user scenarios. KGBridge comprises two core components: a KG-enhanced Prompt Encoder, which models relation-level semantics as soft prompts to provide structured and dynamic priors for user sequence modeling (addressing C1), and a Two-stage Training Paradigm, which combines cross-domain pretraining and privacy-preserving fine-tuning to enable knowledge transfer without user overlap (addressing C2). By combining relation-aware semantic control with correspondence-driven disentanglement, KGBridge explicitly separates and balances domain-shared and domain-specific semantics, thereby maintaining complementarity and stabilizing adaptation during fine-tuning (addressing C3). Extensive experiments on benchmark datasets demonstrate that KGBridge consistently outperforms state-of-the-art baselines and remains robust under varying KG sparsity, highlighting its effectiveness in mitigating structural imbalance and semantic entanglement in KG-enhanced cross-domain recommendation.
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02805v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>MemSearcher：通过端到端强化学习训练大语言模型进行推理、搜索和记忆管理
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            MemSearcher: Training LLMs to Reason, Search and Manage Memory via End-to-End Reinforcement Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qianhao Yuan, Jie Lou, Zichao Li, Jiawei Chen, Yaojie Lu, Hongyu Lin, Le Sun, De...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究搜索代理在多轮交互中上下文长度爆炸的计算效率问题，核心思想是通过端到端强化学习训练LLM维护紧凑记忆，联合优化推理、搜索策略和记忆管理，实现稳定上下文长度而不牺牲信息完整性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对搜索代理的核心效率问题，提出端到端强化学习框架优化推理、搜索和记忆管理，与搜索系统优化高度相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 18:27:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02805v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02805v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Typical search agents concatenate the entire interaction history into the LLM context, preserving information integrity but producing long, noisy contexts, resulting in high computation and memory costs. In contrast, using only the current turn avoids this overhead but discards essential information. This trade-off limits the scalability of search agents. To address this challenge, we propose MemSearcher, an agent workflow that iteratively maintains a compact memory and combines the current turn with it. At each turn, MemSearcher fuses the user's question with the memory to generate reasoning traces, perform search actions, and update memory to retain only information essential for solving the task. This design stabilizes context length across multi-turn interactions, improving efficiency without sacrificing accuracy. To optimize this workflow, we introduce multi-context GRPO, an end-to-end RL framework that jointly optimize reasoning, search strategies, and memory management of MemSearcher Agents. Specifically, multi-context GRPO samples groups of trajectories under different contexts and propagates trajectory-level advantages across all conversations within them. Trained on the same dataset as Search-R1, MemSearcher achieves significant improvements over strong baselines on seven public benchmarks: +11% on Qwen2.5-3B-Instruct and +12% on Qwen2.5-7B-Instruct relative average gains. Notably, the 3B-based MemSearcher even outperforms 7B-based baselines, demonstrating that striking a balance between information integrity and efficiency yields both higher accuracy and lower computational overhead. The code and models will be publicly available at https://github.com/icip-cas/MemSearcher
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02451v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>融合持续预训练模型以构建领域专用大语言模型：金融领域案例研究
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Merging Continual Pretraining Models for Domain-Specialized LLMs: A Case Study in Finance
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kentaro Ueda, François Portet, Hirohiko Suwa, Keiichi Yasumoto
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究如何将不同领域的持续预训练专家模型合并为专业化大语言模型；核心方法是探索任务算术、TIES和DARE-TIES等模型合并技术，通过知识恢复、互补性和涌现性评估框架来构建具备多领域技能的专用模型。</p>
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接研究LLM模型合并技术，属于Transformer架构效率优化和LLM核心技术领域，对构建多技能推荐/搜索系统具有重要参考价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 10:28:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02451v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02451v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    While LLMs excel at general tasks, they struggle in specialized domains like finance, requiring diverse skills in domain knowledge, mathematical reasoning, and multilingual processing. Merging domain-specific Continual Pre-training (CPT) "experts" offers a practical alternative to costly and unstable multi-skill training. However, unlike established Supervised Fine-Tuning (SFT) model-based merging, CPT model merging remains largely unexplored. We address this gap by creating financial LLMs from experts in finance, math, and Japanese. We propose a three-stage evaluation focusing on knowledge recovery, complementarity, and emergence, and assess three merging methods (Task Arithmetic, TIES, and DARE-TIES) on a comprehensive financial benchmark curated from 18 tasks across 8 established datasets. Results show that merging an expert with its base model recovers general knowledge lost during CPT, while merging experts improves performance and can yield emergent cross-domain skills. Among the methods, Task Arithmetic performs strongly but is hyperparameter-sensitive, whereas TIES is more robust. Our findings also suggest that while model similarity correlates with merging success, emergent skills depend on more complex factors. This work presents the first foundational analysis of CPT model merging, establishing a principled framework and providing clear guidance for building multi-skill LLMs from existing assets.
                </div>
            </details>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02360v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>CoCoVa：用于潜在空间推理的连续视觉-语言思维链
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            CoCoVa: Chain of Continuous Vision-Language Thought for Latent Space Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jizheng Ma, Xiaofei Zhou, Yanlong Song, Han Yan
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究视觉语言模型中离散语言处理与连续视觉理解之间的表征鸿沟问题，核心方法是构建迭代推理循环，通过Latent Q-Former在连续潜空间中动态精炼跨模态思维链，实现视觉与语言的深度融合推理。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的连续潜空间推理框架直接类比VLM处理异构数据的思想，其跨模态融合和注意力机制对推荐系统中的多模态数据处理具有重要启发价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 08:28:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02360v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02360v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In human cognition, there exist numerous thought processes that are tacit and beyond verbal expression, enabling us to understand and interact with the world in multiple ways. However, contemporary Vision-Language Models (VLMs) remain constrained to reasoning within the discrete and rigid space of linguistic tokens, thereby bottlenecking the rich, high-dimensional nature of visual perception. To bridge this gap, we propose CoCoVa (Chain of Continuous Vision-Language Thought), a novel framework for vision-language model that leverages continuous cross-modal reasoning for diverse vision-language tasks. The core of CoCoVa is an iterative reasoning cycle, where a novel Latent Q-Former (LQ-Former) acts as a dynamic reasoning engine, iteratively refining a chain of latent thought vectors through cross-modal fusion. To focus this process, a token selection mechanism dynamically identifies salient visual regions, mimicking attentional focus. To ensure these latent thoughts remain grounded, we train the model with a multi-task objective that combines contrastive learning and diffusion-based reconstruction, enforcing alignment between latent representations and both visual and textual modalities. Evaluations show CoCoVa improves accuracy and token efficiency over strong baselines. With a 1.5B backbone, it competes with or surpasses larger 7B-9B models on almost all benchmarks. When scaled to 7B LLM backbones, it remains competitive with state-of-the-art models. Qualitative analysis validates that learned latent space captures interpretable and structured reasoning patterns, highlighting the potential of CoCoVa to bridge the representational gap between discrete language processing and the continuous nature of visual understanding.
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02208v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>训练主动式和个性化的大语言模型智能体
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Training Proactive and Personalized LLM Agents
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Weiwei Sun, Xuhui Zhou, Weihua Du, Xingyao Wang, Sean Welleck, Graham Neubig, Ma...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何构建更有效的现实世界AI助手，核心思想是通过多目标强化学习同时优化任务完成度、主动提问能力和个性化适应能力，将用户偏好建模为可配置的交互维度进行联合训练。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的多目标优化框架直接针对个性化推荐和用户交互的核心问题，其主动提问和个性化适应机制对搜索推荐系统具有直接应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 02:59:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02208v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02208v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    While existing work focuses primarily on task success, we argue that effective real-world agents require optimizing three dimensions: productivity (task completion), proactivity (asking essential questions), and personalization (adapting to diverse user preferences). We introduce UserVille, an interactive environment with LLM-based user simulators enabling diverse, configurable user preferences. Leveraging UserVille, we introduce PPP, a multi-objective reinforcement learning approach that jointly optimizes all three dimensions: Productivity, Proactivity, and Personalization. Experiments on software engineering and deep research tasks show that agents trained with PPP achieve substantial improvements over strong baselines such as GPT-5 (+21.6 on average), demonstrating the ability to ask strategic clarifying questions, adapt to unseen user preferences, and improve task success through better interaction. This work demonstrates that explicitly optimizing for user-centered interaction is critical for building practical and effective AI agents.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02194v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>个性化决策建模：效用优化与文本化符号推理
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Personalized Decision Modeling: Utility Optimization or Textualized-Symbolic Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yibo Zhao, Yang Zhao, Hongru Du, Hao Frank Yang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究如何建模个体决策过程与群体最优预测之间的差异；核心方法是提出ATHENA框架，通过LLM增强的符号发现构建群体级效用函数，再通过语义适配创建个性化语义模板来建模个体选择。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接应用LLM技术于个性化决策建模，提出符号化推理与语义适配的双阶段框架，完美契合个性化推荐系统的核心需求。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 02:19:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02194v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02194v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CY</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Decision-making models for individuals, particularly in high-stakes scenarios like vaccine uptake, often diverge from population optimal predictions. This gap arises from the uniqueness of the individual decision-making process, shaped by numerical attributes (e.g., cost, time) and linguistic influences (e.g., personal preferences and constraints). Developing upon Utility Theory and leveraging the textual-reasoning capabilities of Large Language Models (LLMs), this paper proposes an Adaptive Textual-symbolic Human-centric Reasoning framework (ATHENA) to address the optimal information integration. ATHENA uniquely integrates two stages: First, it discovers robust, group-level symbolic utility functions via LLM-augmented symbolic discovery; Second, it implements individual-level semantic adaptation, creating personalized semantic templates guided by the optimal utility to model personalized choices. Validated on real-world travel mode and vaccine choice tasks, ATHENA consistently outperforms utility-based, machine learning, and other LLM-based models, lifting F1 score by at least 6.5% over the strongest cutting-edge models. Further, ablation studies confirm that both stages of ATHENA are critical and complementary, as removing either clearly degrades overall predictive performance. By organically integrating symbolic utility modeling and semantic adaptation, ATHENA provides a new scheme for modeling human-centric decisions. The project page can be found at https://yibozh.github.io/Athena.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02770v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>超越单一嵌入：通过多查询检索捕获多样化目标
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Beyond Single Embeddings: Capturing Diverse Targets with Multi-Query Retrieval
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hung-Ting Chen, Xiang Liu, Shauli Ravfogel, Eunsol Choi
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究单一查询向量无法捕捉相关文档多模态分布的问题，核心思想是自回归生成多个查询向量来检索多样化目标文档，实现更全面的检索覆盖。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出多查询向量检索方法，直接解决推荐和搜索中查询多义性和目标多样性的核心问题，与多模态统一建模理念高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 17:57:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02770v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02770v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.IR</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Most text retrievers generate \emph{one} query vector to retrieve relevant documents. Yet, the conditional distribution of relevant documents for the query may be multimodal, e.g., representing different interpretations of the query. We first quantify the limitations of existing retrievers. All retrievers we evaluate struggle more as the distance between target document embeddings grows. To address this limitation, we develop a new retriever architecture, \emph{A}utoregressive \emph{M}ulti-\emph{E}mbedding \emph{R}etriever (AMER). Our model autoregressively generates multiple query vectors, and all the predicted query vectors are used to retrieve documents from the corpus. We show that on the synthetic vectorized data, the proposed method could capture multiple target distributions perfectly, showing 4x better performance than single embedding model. We also fine-tune our model on real-world multi-answer retrieval datasets and evaluate in-domain. AMER presents 4 and 21\% relative gains over single-embedding baselines on two datasets we evaluate on. Furthermore, we consistently observe larger gains on the subset of dataset where the embeddings of the target documents are less similar to each other. We demonstrate the potential of using a multi-query vector retriever and open up a new direction for future work.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02358v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>让多模态嵌入器通过自适应查询增强学习何时增强查询
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Let Multimodal Embedders Learn When to Augment Query via Adaptive Query Augmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wongyu Kim, Hochang Lee, Sanghak Lee, Yoonsung Kim, Jaehyun Park
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究多模态搜索中查询增强的效率和性能问题，核心思想是训练多模态嵌入器自适应决定何时进行查询增强，通过生成特定前缀来区分需要增强和直接嵌入的查询。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对搜索领域的查询增强问题，提出自适应增强机制解决LLM嵌入器的延迟和性能问题，并扩展到多模态环境，与搜索和LLM应用高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 08:24:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02358v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02358v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.IR</span><span class="category-tag">cs.LG</span><span class="category-tag">cs.MM</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Query augmentation makes queries more meaningful by appending further information to the queries to find relevant documents. Current studies have proposed Large Language Model (LLM)-based embedders, which learn representation for embedding and generation for query augmentation in a multi-task manner by leveraging the generative capabilities of LLM. During inference, these jointly trained embedders have conducted query augmentation followed by embedding, showing effective results. However, augmenting every query leads to substantial embedding latency and query augmentation can be detrimental to performance for some queries. Also, previous methods have not been explored in multimodal environments. To tackle these problems, we propose M-Solomon, a universal multimodal embedder that can adaptively determine when to augment queries. Our approach first divides the queries of the training datasets into two groups at the dataset level. One includes queries that require augmentation and the other includes queries that do not. Then, we introduces a synthesis process that generates appropriate augmentations for queries that require them by leveraging a powerful Multimodal LLM (MLLM). Next, we present adaptive query augmentation. Through this step, M-Solomon can conduct query augmentation only when necessary by learning to generate synthetic augmentations with the prefix /augment for queries that demand them and to generate the simple string /embed for others. Experimental results showed that M-Solomon not only surpassed the baseline without augmentation by a large margin but also outperformed the baseline that always used augmentation, providing much faster embedding latency.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02755v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>基于强化学习控制集中式多智能体大语言模型系统的性能与预算
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Controlling Performance and Budget of a Centralized Multi-agent LLM System with Reinforcement Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bowen Jin, TJ Collins, Donghan Yu, Mert Cemri, Shenao Zhang, Mengyu Li, Jay Tang...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究多专家LLM系统的成本控制问题，核心思想是通过强化学习训练集中控制器，在预算约束下动态选择专家模型以优化性能-成本权衡。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出集中式多LLM系统的强化学习控制框架，直接解决推荐系统中成本控制与性能平衡的核心问题，方法具有通用适用性。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 17:35:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02755v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02755v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) exhibit complementary strengths across domains and come with varying inference costs, motivating the design of multi-agent LLM systems where specialized models collaborate efficiently. Existing approaches predominantly rely on decentralized frameworks, which invoke multiple LLMs for every input and thus lead to substantial and uncontrolled inference costs. In this work, we introduce a centralized multi-LLM framework, where a controller LLM selectively coordinates a pool of expert models in a cost-efficient and cost-controllable manner. We formulate this coordination problem as reinforcement learning with dual objectives: maximizing task performance while minimizing the overall inference cost. In addition, we expect the multi-agent system to have adapted behavior with different budget conditions during inference. To this end, we propose CoRL, a reinforcement learning framework that optimizes the performance cost trade-off in a controllable multi-budget setting. Experiments on four diverse benchmarks demonstrate that CoRL enables a single system to surpass the best expert LLM under high-budget settings, while maintaining strong performance in more economical low-budget modes, highlighting the effectiveness of centralized coordination for scalable and cost-efficient multi-agent LLM systems.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02681v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>最优奇异损伤：低存储机制下的高效大语言模型推理
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Optimal Singular Damage: Efficient LLM Inference in Low Storage Regimes
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mohammadsajad Alipour, Mohammad Mohammadi Amiri
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究如何高效存储预训练模型微调后的参数更新；核心方法是利用参数更新的低秩和稀疏特性，提出最优奇异值损伤技术，通过选择性稀疏化低秩近似来保留最关键的分量。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出高效存储微调参数的方法，直接解决LLM在推荐系统等应用中的存储瓶颈问题，属于核心LLM技术进步。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 16:05:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02681v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02681v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) are increasingly prevalent across diverse applications. However, their enormous size limits storage and processing capabilities to a few well-resourced stakeholders. As a result, most applications rely on pre-trained LLMs, fine-tuned for specific tasks. However, even storing the fine-tuned versions of these models remains a significant challenge due to the wide range of tasks they address. Recently, studies show that fine-tuning these models primarily affects a small fraction of parameters, highlighting the need for more efficient storage of fine-tuned models. This paper focuses on efficient storage of parameter updates in pre-trained models after fine-tuning. To address this challenge, we leverage the observation that fine-tuning updates are both low-rank and sparse, which can be utilized for storage efficiency. However, using only low-rank approximation or sparsification may discard critical singular components that enhance model expressivity. We first observe that given the same memory budget, sparsified low-rank approximations with larger ranks outperform standard low-rank approximations with smaller ranks. Building on this, we propose our method, optimal singular damage, that selectively sparsifies low-rank approximated updates by leveraging the interleaved importance of singular vectors, ensuring that the most impactful components are retained. We demonstrate through extensive experiments that our proposed methods lead to significant storage efficiency and superior accuracy within the same memory budget compared to employing the low-rank approximation or sparsification individually.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02213v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>IG-Pruning：面向大语言模型的输入引导块剪枝
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            IG-Pruning: Input-Guided Block Pruning for Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kangyu Qiao, Shaolei Zhang, Yang Feng
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究LLM推理效率问题，核心思想是通过输入感知的动态层剪枝方法，根据具体输入动态选择Transformer层掩码，避免固定剪枝模式在不同任务和输入下的性能损失。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的输入感知动态剪枝方法直接针对LLM推理效率问题，属于Transformer架构效率优化的重要进展，对推荐和搜索系统的实际部署具有直接应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 03:05:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02213v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02213v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    With the growing computational demands of large language models (LLMs), efficient inference has become increasingly critical for practical deployment. Depth pruning has emerged as a promising approach for reducing the computational costs of large language models by removing transformer layers. However, existing methods typically rely on fixed block masks, which can lead to suboptimal performance across different tasks and inputs. In this paper, we propose IG-Pruning, a novel input-aware block-wise pruning method that dynamically selects layer masks at inference time. Our approach consists of two stages: (1) Discovering diverse mask candidates through semantic clustering and L0 optimization, and (2) Implementing efficient dynamic pruning without the need for extensive training. Experimental results demonstrate that our method consistently outperforms state-of-the-art static depth pruning methods, making it particularly suitable for resource-constrained deployment scenarios.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02833v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>在良好的GRACEs中：面向知识蒸馏的原则性教师选择
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            In Good GRACEs: Principled Teacher Selection for Knowledge Distillation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Abhishek Panigrahi, Bingbin Liu, Sadhika Malladi, Sham Kakade, Surbhi Goel
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究知识蒸馏中教师模型选择的核心问题，提出GRACE评分方法通过分析学生模型梯度的分布特性来预测教师模型的兼容性，无需访问验证器或教师模型内部信息。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出GRACE评分方法解决知识蒸馏中教师模型选择问题，属于LLM技术优化方向，对推荐系统中模型压缩和部署有直接应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 18:58:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02833v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02833v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Knowledge distillation is an efficient strategy to use data generated by large "teacher" language models to train smaller capable "student" models, but selecting the optimal teacher for a specific student-task combination requires expensive trial-and-error. We propose a lightweight score called GRACE to quantify how effective a teacher will be for post-training a student model. GRACE measures distributional properties of the student's gradients without access to a verifier, teacher logits, teacher internals, or test data. From an information-theoretic perspective, GRACE connects to leave-one-out stability of gradient-based algorithms, which controls the generalization performance of the distilled students. On GSM8K and MATH, GRACE correlates strongly (up to 86% Spearman correlation) with the performance of the distilled LLaMA and OLMo students. In particular, training a student using the GRACE-selected teacher can improve the performance by up to 7.4% over naively using the best-performing teacher. Further, GRACE can provide guidance on crucial design choices in distillation, including (1) the best temperature to use when generating from the teacher, (2) the best teacher to use given a size constraint, and (3) the best teacher to use within a specific model family. Altogether, our findings demonstrate that GRACE can efficiently and effectively identify a strongly compatible teacher for a given student and provide fine-grained guidance on how to perform distillation.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02652v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>可微分分层视觉标记化
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Differentiable Hierarchical Visual Tokenization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Marius Aasan, Martine Hjelkrem-Tan, Nico Catalano, Changkyu Choi, Adín Ramírez R...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究视觉Transformer中固定图像块标记忽略空间语义结构的问题，核心方法是开发端到端可微分标记器，通过分层模型选择自适应图像内容，保持与现有架构的向后兼容性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出可微分分层视觉标记化方法，直接改进Transformer架构的输入处理效率，对推荐系统中处理图像和序列数据有重要借鉴意义。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 15:18:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02652v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02652v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">68T45</span><span class="category-tag">I.2.10; I.4.10; I.4.6; I.3.8</span></div>
            </div>
            
            
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                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Vision Transformers rely on fixed patch tokens that ignore the spatial and semantic structure of images. In this work, we introduce an end-to-end differentiable tokenizer that adapts to image content with pixel-level granularity while remaining backward-compatible with existing architectures for retrofitting pretrained models. Our method uses hierarchical model selection with information criteria to provide competitive performance in both image-level classification and dense-prediction tasks, and even supports out-of-the-box raster-to-vector conversion.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02650v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>视觉输入能否被压缩？面向大型多模态模型的视觉令牌压缩基准
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Can Visual Input Be Compressed? A Visual Token Compression Benchmark for Large Multimodal Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tianfan Peng, Yuntao Du, Pengzhou Ji, Shijie Dong, Kailin Jiang, Mingchuan Ma, Y...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究大型多模态模型中视觉token冗余导致的推理效率低下问题，核心方法是建立统一基准UniPruneBench来系统评估各种视觉token剪枝算法的效果，发现剪枝比例是影响性能的主要因素。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视觉token压缩技术，直接关联Transformer架构效率优化，对多模态推荐和搜索系统具有重要应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 15:17:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02650v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02650v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large multimodal models (LMMs) often suffer from severe inference inefficiency due to the large number of visual tokens introduced by image encoders. While recent token compression methods, such as pruning and merging, have shown promise in reducing redundancy, their evaluation remains fragmented and inconsistent. In this work, we present UniPruneBench, a unified and extensible benchmark for visual token pruning in multimodal LLMs. UniPruneBench provides standardized protocols across six ability dimensions and ten datasets, covering ten representative compression algorithms and three families of LMMs (LLaVA-v1.5, Intern-VL3, and Qwen2.5-VL). Beyond task accuracy, it incorporates system-level metrics such as runtime and prefilling latency to provide a holistic view. Our experiments uncover several key findings: (1) random pruning is a surprisingly strong baseline, (2) no single method consistently outperforms others across scenarios, (3) pruning sensitivity varies significantly across tasks, with OCR being most vulnerable, and (4) pruning ratio is the dominant factor governing performance degradation. We believe UniPruneBench will serve as a reliable foundation for future research on efficient multimodal modeling.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02817v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>Oolong：评估长上下文推理与聚合能力
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>6/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Oolong: Evaluating Long Context Reasoning and Aggregation Capabilities
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Amanda Bertsch, Adithya Pratapa, Teruko Mitamura, Graham Neubig, Matthew R. Gorm...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究现有长上下文评估基准过度依赖检索任务、无法全面评估模型推理能力的问题，核心思想是构建需要逐段分析文本并聚合信息回答分布性问题的评估基准，包含合成任务和真实对话数据两种场景。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于长上下文推理评估基准开发，虽然不直接涉及推荐系统核心算法，但长上下文处理能力对处理用户历史序列和复杂上下文特征的推荐模型至关重要。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 18:42:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02817v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02817v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    As model context lengths continue to grow, concerns about whether models effectively use the full context length have persisted. While several carefully designed long-context evaluations have recently been released, these evaluations tend to rely on retrieval from one or more sections of the context, which allows nearly all of the context tokens to be disregarded as noise. This represents only one type of task that might be performed with long context. We introduce Oolong, a benchmark of long-context reasoning tasks that require analyzing individual chunks of text on an atomic level, and then aggregating these analyses to answer distributional questions. Oolong is separated into two task sets: Oolong-synth, a set of naturalistic synthetic tasks, where we can easily ablate components of the reasoning problem; and Oolong-real, a downstream setting which requires reasoning over real-world conversational data. Oolong requires models to reason over large quantities of examples, to perform both classification and counting in-context, and to reason over temporal and user relations. Even frontier models struggle on Oolong, with GPT-5, Claude-Sonnet-4, and Gemini-2.5-Pro all achieving less than 50% accuracy on both splits at 128K. We release the data and evaluation harness for Oolong to enable further development of models that can reason over large quantities of text.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02303v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>解锁多智能体大语言模型在推理中的潜力：从惰性智能体到深思熟虑
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Unlocking the Power of Multi-Agent LLM for Reasoning: From Lazy Agents to Deliberation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhiwei Zhang, Xiaomin Li, Yudi Lin, Hui Liu, Ramraj Chandradevan, Linlin Wu, Min...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究多智能体LLM协作中出现的懒惰行为问题，核心方法是引入可验证奖励机制和因果影响度量来促进智能体间的有效协作与审议推理。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究多智能体LLM协作中的懒惰行为问题，虽然涉及LLM技术，但核心关注通用推理任务而非搜索推荐广告领域的特定应用。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 06:37:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02303v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02303v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) trained with reinforcement learning and verifiable rewards have achieved strong results on complex reasoning tasks. Recent work extends this paradigm to a multi-agent setting, where a meta-thinking agent proposes plans and monitors progress while a reasoning agent executes subtasks through sequential conversational turns. Despite promising performance, we identify a critical limitation: lazy agent behavior, in which one agent dominates while the other contributes little, undermining collaboration and collapsing the setup to an ineffective single agent. In this paper, we first provide a theoretical analysis showing why lazy behavior naturally arises in multi-agent reasoning. We then introduce a stable and efficient method for measuring causal influence, helping mitigate this issue. Finally, as collaboration intensifies, the reasoning agent risks getting lost in multi-turn interactions and trapped by previous noisy responses. To counter this, we propose a verifiable reward mechanism that encourages deliberation by allowing the reasoning agent to discard noisy outputs, consolidate instructions, and restart its reasoning process when necessary. Extensive experiments demonstrate that our framework alleviates lazy agent behavior and unlocks the full potential of multi-agent framework for complex reasoning tasks.
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            </details>
    </div>
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            <a href="https://www.alphaxiv.org/abs/2511.02711v1" target="_blank" rel="noopener noreferrer">
                关系深度挖掘：面向非结构化数据的错误感知查询
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Relational Deep Dive: Error-Aware Queries Over Unstructured Data
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Daren Chao, Kaiwen Chen, Naiqing Guan, Nick Koudas
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注非结构化数据查询和错误处理，这与推荐系统/搜索的核心排名任务关联度较低。虽然错误感知机制在工业系统中具有通用价值，但论文没有明确展示其在RecSys/Search/Ads中的具体应用潜力，且未涉及LLM、Transformer或异构数据建模等关键技术方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 16:30:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02711v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02711v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.DB</span><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Unstructured data is pervasive, but analytical queries demand structured representations, creating a significant extraction challenge. Existing methods like RAG lack schema awareness and struggle with cross-document alignment, leading to high error rates. We propose ReDD (Relational Deep Dive), a framework that dynamically discovers query-specific schemas, populates relational tables, and ensures error-aware extraction with provable guarantees. ReDD features a two-stage pipeline: (1) Iterative Schema Discovery (ISD) identifies minimal, joinable schemas tailored to each query, and (2) Tabular Data Population (TDP) extracts and corrects data using lightweight classifiers trained on LLM hidden states. A main contribution of ReDD is SCAPE, a statistically calibrated method for error detection with coverage guarantees, and SCAPE-HYB, a hybrid approach that optimizes the trade-off between accuracy and human correction costs. Experiments across diverse datasets demonstrate ReDD's effectiveness, reducing data extraction errors from up to 30% to below 1% while maintaining high schema completeness (100% recall) and precision. ReDD's modular design enables fine-grained control over accuracy-cost trade-offs, making it a robust solution for high-stakes analytical queries over unstructured corpora.
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            <a href="https://www.alphaxiv.org/abs/2511.02834v1" target="_blank" rel="noopener noreferrer">
                Agent-Omni：通过模型协调实现测试时多模态推理以理解万物
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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            Agent-Omni: Test-Time Multimodal Reasoning via Model Coordination for Understanding Anything
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Huawei Lin, Yunzhi Shi, Tong Geng, Weijie Zhao, Wei Wang, Ravender Pal Singh
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态推理和模型协调，虽然多模态处理与VLM类比有潜在关联，但标题强调'理解万物'的通用能力，缺乏明确的推荐系统、搜索或广告应用场景。测试时推理机制可能对实时推荐系统有启发，但具体应用路径不明确。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 18:59:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02834v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02834v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                    Multimodal large language models (MLLMs) have shown strong capabilities but remain limited to fixed modality pairs and require costly fine-tuning with large aligned datasets. Building fully omni-capable models that can integrate text, images, audio, and video remains impractical and lacks robust reasoning support. In this paper, we propose an Agent-Omni framework that coordinates existing foundation models through a master-agent system, enabling flexible multimodal reasoning without retraining. The master agent interprets user intent, delegates subtasks to modality-specific agents, and integrates their outputs into coherent responses. Extensive experiments across text, image, audio, video, and omni benchmarks show that Agent-Omni consistently achieves state-of-the-art performance, particularly on tasks requiring complex cross-modal reasoning. Its agent-based design enables seamless integration of specialized foundation models, ensuring adaptability to diverse inputs while maintaining transparency and interpretability. In addition, the framework is modular and easily extensible, allowing future improvements as stronger models become available. %We release an open-source implementation to support continued research on scalable and reliable omni-modal reasoning.
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            <a href="https://www.alphaxiv.org/abs/2511.02795v1" target="_blank" rel="noopener noreferrer">
                大型语言模型能进行数字减法运算吗？
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            Can LLMs subtract numbers?
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mayank Jobanputra, Nils Philipp Walter, Maitrey Mehta, Blerta Veseli, Evan Parke...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文探讨LLMs的基本数学能力，这属于LLM核心能力评估范畴。虽然数学推理能力对推荐/搜索系统中的数值计算和逻辑推理有潜在应用价值，但论文标题显示其更侧重于基础能力测试而非具体应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 18:20:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02795v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02795v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CL</span></div>
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                    We present a systematic study of subtraction in large language models (LLMs). While prior benchmarks emphasize addition and multiplication, subtraction has received comparatively little attention despite being structurally distinct as a non-commutative operation. We evaluate eight pretrained LLMs spanning four families on addition and subtraction problems. Our experiments reveal that subtraction accuracy lags behind addition by a wide margin. We find that the errors for ($a-b$) are concentrated in cases where ($a<b$). In such cases, LLMs frequently produce the correct magnitude but omit the negative sign. Probing analyses show that LLMs internally encode whether results should be negative, yet this information is often not reflected in generated outputs. We further test well-known techniques such as few-shot learning and instruction-tuning to see if they can improve the LLMs' performance. Our results suggest that while few-shot prompting yields modest gains, the instruction-tuned models achieve near-perfect accuracies in generating the negative sign. Together, these findings provide a clearer characterization of the limitations and recoverability of LLMs' arithmetic capabilities in subtraction.
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            <a href="https://www.alphaxiv.org/abs/2511.02778v1" target="_blank" rel="noopener noreferrer">
                VCode：一种以SVG作为符号化视觉表示的多模态编码基准
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            VCode: a Multimodal Coding Benchmark with SVG as Symbolic Visual Representation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kevin Qinghong Lin, Yuhao Zheng, Hangyu Ran, Dantong Zhu, Dongxing Mao, Linjie L...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态编码基准和SVG作为符号视觉表示，这与VLM类比处理异构数据的理念有一定关联，但缺乏明确的推荐系统、搜索或广告应用场景。虽然符号表示可能为处理结构化用户数据提供启发，但论文焦点更偏向通用多模态基准而非具体应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 18:00:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02778v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02778v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span></div>
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                    Code has emerged as a precise and executable medium for reasoning and action in the agent era. Yet, progress has largely focused on language-centric tasks such as program synthesis and debugging, leaving visual-centric coding underexplored. Inspired by how humans reason over sketches, we advocate SVG code as a compact, interpretable, and executable visual representation. We introduce VCode, a benchmark that reframes multimodal understanding as code generation: given an image, a model must produce SVG that preserves symbolic meaning for downstream reasoning. VCode covers three domains - general commonsense (MM-Vet), professional disciplines (MMMU), and visual-centric perception (CV-Bench). To assess symbolic fidelity, we propose CodeVQA, a novel evaluation protocol in which a policy model answers questions over rendered SVGs; correct answers indicate faithful symbolic preservation. Empirically, frontier VLMs struggle to generate faithful SVGs, revealing a persistent gap between language-centric and visual-centric coding. To close this gap, we introduce VCoder, an agentic framework that augments VLMs along two axes: (i) Thinking with Revision, which iteratively analyzes discrepancies and refines SVG code; and (ii) Acting with Visual Tools, where detectors and parsers supply structured cues such as objects, shapes, and text beyond the model's intrinsic capacity. Across benchmarks, frontier VLMs with strong reasoning capabilities score well overall yet remain limited in professional knowledge and 3D reasoning. VCoder delivers a 12.3-point overall gain over the top-performing Claude-4-Opus. Human studies show that both humans and VLMs perform worse on rendered SVGs, their consistency reveals the promise of symbolic visual representation. The benchmark and code are available at https://github.com/CSU-JPG/VCode.
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            <a href="https://www.alphaxiv.org/abs/2511.02734v1" target="_blank" rel="noopener noreferrer">
                CostBench：评估LLM工具使用代理在动态环境中的多轮成本最优规划与适应能力
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            CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiayu Liu, Cheng Qian, Zhaochen Su, Qing Zong, Shijue Huang, Bingxiang He, Yi R....
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM工具使用代理的成本优化规划，这属于LLM应用技术范畴。虽然成本优化在推荐系统和广告中有潜在应用价值（如资源分配优化），但论文焦点在通用工具使用代理而非特定于RecSys/Search/Ads领域，且动态环境规划与核心推荐/搜索/广告技术关联度较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 16:58:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02734v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02734v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    Current evaluations of Large Language Model (LLM) agents primarily emphasize task completion, often overlooking resource efficiency and adaptability. This neglects a crucial capability: agents' ability to devise and adjust cost-optimal plans in response to changing environments. To bridge this gap, we introduce CostBench, a scalable, cost-centric benchmark designed to evaluate agents' economic reasoning and replanning abilities. Situated in the travel-planning domain, CostBench comprises tasks solvable via multiple sequences of atomic and composite tools with diverse, customizable costs. It also supports four types of dynamic blocking events, such as tool failures and cost changes, to simulate real-world unpredictability and necessitate agents to adapt in real time. Evaluating leading open-sourced and proprietary models on CostBench reveals a substantial gap in cost-aware planning: agents frequently fail to identify cost-optimal solutions in static settings, with even GPT-5 achieving less than 75% exact match rate on the hardest tasks, and performance further dropping by around 40% under dynamic conditions. By diagnosing these weaknesses, CostBench lays the groundwork for developing future agents that are both economically rational and robust.
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            <a href="https://www.alphaxiv.org/abs/2511.02607v1" target="_blank" rel="noopener noreferrer">
                UniChange：基于多模态大语言模型的统一变化检测
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            UniChange: Unifying Change Detection with Multimodal Large Language Model
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xu Zhang, Danyang Li, Xiaohang Dong, Tianhao Wu, Hualong Yu, Jianye Wang, Qichen...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然涉及多模态大语言模型，但主要聚焦于变化检测这一计算机视觉任务，与推荐系统、搜索或广告的核心领域进展关联较弱。多模态统一建模的理念在视觉-语言模型类比异构数据方面有一定启发，但变化检测本身在RecSys/Search/Ads中的直接应用场景有限。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 14:31:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02607v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02607v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Change detection (CD) is a fundamental task for monitoring and analyzing land cover dynamics. While recent high performance models and high quality datasets have significantly advanced the field, a critical limitation persists. Current models typically acquire limited knowledge from single-type annotated data and cannot concurrently leverage diverse binary change detection (BCD) and semantic change detection (SCD) datasets. This constraint leads to poor generalization and limited versatility. The recent advancements in Multimodal Large Language Models (MLLMs) introduce new possibilities for a unified CD framework. We leverage the language priors and unification capabilities of MLLMs to develop UniChange, the first MLLM-based unified change detection model. UniChange integrates generative language abilities with specialized CD functionalities. Our model successfully unifies both BCD and SCD tasks through the introduction of three special tokens: [T1], [T2], and [CHANGE]. Furthermore, UniChange utilizes text prompts to guide the identification of change categories, eliminating the reliance on predefined classification heads. This design allows UniChange to effectively acquire knowledge from multi-source datasets, even when their class definitions conflict. Experiments on four public benchmarks (WHU-CD, S2Looking, LEVIR-CD+, and SECOND) demonstrate SOTA performance, achieving IoU scores of 90.41, 53.04, 78.87, and 57.62, respectively, surpassing all previous methods. The code is available at https://github.com/Erxucomeon/UniChange.
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            <a href="https://www.alphaxiv.org/abs/2511.02603v1" target="_blank" rel="noopener noreferrer">
                CGES：基于置信度引导的早期停止，用于高效且准确的自一致性
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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            CGES: Confidence-Guided Early Stopping for Efficient and Accurate Self-Consistency
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ehsan Aghazadeh, Ahmad Ghasemi, Hedyeh Beyhaghi, Hossein Pishro-Nik
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM推理效率优化技术（早期停止），属于Enabling LLM Tech范畴。虽然自一致性是LLM推理中的重要技术，但该工作主要聚焦于计算效率优化，在搜索和推荐系统中可用于降低多路径推理的计算成本。然而，其应用场景相对有限，对核心推荐/搜索算法改进的直接贡献较小。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 14:25:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02603v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02603v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) are often queried multiple times at test time, with predictions aggregated by majority vote. While effective, this self-consistency strategy (arXiv:2203.11171) requires a fixed number of calls and can fail when the correct answer is rare. We introduce Confidence-Guided Early Stopping (CGES), a Bayesian framework that forms posteriors over candidate answers using scalar confidence signals derived from token probabilities or reward models. CGES adaptively halts sampling once the posterior mass of a candidate exceeds a threshold. We provide theoretical guarantees for both perfectly calibrated confidences and realistic noisy confidence signals. Across five reasoning benchmarks, CGES reduces the average number of model calls by about 69 percent (for example, from 16.0 to 4.9) while matching the accuracy of self-consistency within 0.06 percentage points.
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            <a href="https://www.alphaxiv.org/abs/2511.02537v1" target="_blank" rel="noopener noreferrer">
                智能招聘：一个可解释的端到端简历信息抽取与职位匹配管道
            </a>
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            Smart-Hiring: An Explainable end-to-end Pipeline for CV Information Extraction and Job Matching
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kenza Khelkhal, Dihia Lanasri
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注简历信息抽取和职位匹配，这属于招聘领域的特定应用，而非通用的推荐系统或搜索技术。虽然涉及信息抽取和匹配，但其应用场景过于狭窄，与当前关注的RecSys核心进展、LLM使能技术或Transformer架构创新缺乏直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 12:44:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02537v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02537v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Hiring processes often involve the manual screening of hundreds of resumes for each job, a task that is time and effort consuming, error-prone, and subject to human bias. This paper presents Smart-Hiring, an end-to-end Natural Language Processing (NLP) pipeline de- signed to automatically extract structured information from unstructured resumes and to semantically match candidates with job descriptions. The proposed system combines document parsing, named-entity recognition, and contextual text embedding techniques to capture skills, experience, and qualifications. Using advanced NLP technics, Smart-Hiring encodes both resumes and job descriptions in a shared vector space to compute similarity scores between candidates and job postings. The pipeline is modular and explainable, allowing users to inspect extracted entities and matching rationales. Experiments were conducted on a real-world dataset of resumes and job descriptions spanning multiple professional domains, demonstrating the robustness and feasibility of the proposed approach. The system achieves competitive matching accuracy while preserving a high degree of interpretability and transparency in its decision process. This work introduces a scalable and practical NLP frame- work for recruitment analytics and outlines promising directions for bias mitigation, fairness-aware modeling, and large-scale deployment of data-driven hiring solutions.
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            <a href="https://www.alphaxiv.org/abs/2511.02280v1" target="_blank" rel="noopener noreferrer">
                SAIL-RL：通过双奖励强化学习调优指导多模态大语言模型在何时及如何思考
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            SAIL-RL: Guiding MLLMs in When and How to Think via Dual-Reward RL Tuning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Fangxun Shu, Yongjie Ye, Yue Liao, Zijian Kang, Weijie Yin, Jiacong Wang, Xiao L...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态大语言模型（MLLMs）的推理过程优化，属于纯粹的LLM推理技术范畴。虽然强化学习调优方法在技术上具有通用性，但论文标题未表明与推荐系统、搜索或广告领域的直接关联，也未展示对异构数据处理或Transformer架构改进的明确应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 05:34:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02280v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02280v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span></div>
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                    We introduce SAIL-RL, a reinforcement learning (RL) post-training framework that enhances the reasoning capabilities of multimodal large language models (MLLMs) by teaching them when and how to think. Existing approaches are limited by outcome-only supervision, which rewards correct answers without ensuring sound reasoning, and by uniform thinking strategies, which often lead to overthinking on simple tasks and underthinking on complex ones. SAIL-RL addresses these challenges with a dual reward system: the Thinking Reward, which evaluates reasoning quality through factual grounding, logical coherence, and answer consistency, and the Judging Reward, which adaptively determines whether deep reasoning or direct answering is appropriate. Experiments on the state-of-the-art SAIL-VL2 show that SAIL-RL improves reasoning and multimodal understanding benchmarks at both 4B and 8B scales, achieving competitive performance against commercial closed-source models such as GPT-4o, and substantially reduces hallucinations, establishing it as a principled framework for building more reliable and adaptive MLLMs. The code will be available at https://github.com/BytedanceDouyinContent/SAIL-RL.
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            <a href="https://www.alphaxiv.org/abs/2511.02205v1" target="_blank" rel="noopener noreferrer">
                OmniField：用于鲁棒多模态时空学习的条件化神经场
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            OmniField: Conditioned Neural Fields for Robust Multimodal Spatiotemporal Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kevin Valencia, Thilina Balasooriya, Xihaier Luo, Shinjae Yoo, David Keetae Park
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及神经场和多模态时空学习，可能属于Transformer架构效率或新架构的进展。然而，标题没有明确表明与推荐系统、搜索或广告的直接联系，且多模态可能偏向视觉或其他领域，而非明确的异构数据建模。潜在应用可能包括处理时空用户行为数据，但相关性较弱。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 02:50:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02205v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02205v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                    Multimodal spatiotemporal learning on real-world experimental data is constrained by two challenges: within-modality measurements are sparse, irregular, and noisy (QA/QC artifacts) but cross-modally correlated; the set of available modalities varies across space and time, shrinking the usable record unless models can adapt to arbitrary subsets at train and test time. We propose OmniField, a continuity-aware framework that learns a continuous neural field conditioned on available modalities and iteratively fuses cross-modal context. A multimodal crosstalk block architecture paired with iterative cross-modal refinement aligns signals prior to the decoder, enabling unified reconstruction, interpolation, forecasting, and cross-modal prediction without gridding or surrogate preprocessing. Extensive evaluations show that OmniField consistently outperforms eight strong multimodal spatiotemporal baselines. Under heavy simulated sensor noise, performance remains close to clean-input levels, highlighting robustness to corrupted measurements.
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            <a href="https://www.alphaxiv.org/abs/2511.02182v1" target="_blank" rel="noopener noreferrer">
                定位视频问答中的触发时刻：增强多模态大语言模型中的时空定位能力
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            Pinpointing Trigger Moment for Grounded Video QA: Enhancing Spatio-temporal Grounding in Multimodal Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jinhwan Seo, Yoonki Cho, Junhyug Noh, Sung-eui Yoon
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频问答中的时空定位问题，属于视觉-语言多模态研究范畴。虽然涉及多模态大语言模型，但其核心应用场景是视频理解而非推荐系统、搜索或广告领域。论文的技术方法可能对处理序列化用户行为数据有所启发，但这种关联性较弱且间接。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 01:50:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02182v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02182v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In this technical report, we introduce a framework to address Grounded Video Question Answering (GVQA) task for the ICCV 2025 Perception Test Challenge. The GVQA task demands robust multimodal models capable of complex reasoning over video content, grounding the resulting answers visually, and tracking the referenced objects temporally. To achieve this capability, our proposed approach decomposes the GVQA task into a three-stage pipeline: (1) Video Reasoning \& QA, (2) Spatio-temporal Grounding and (3) Tracking. Our key contribution is the introduction of a trigger moment, derived from our proposed CORTEX prompt, which pinpoints the single most visible frame of a target object to serve as a robust anchor for grounding and tracking. To this end, we achieve the HOTA score of 0.4968, which marks a significant improvement over the previous year's winning score of 0.2704 on GVQA task.
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            <a href="https://www.alphaxiv.org/abs/2511.02571v1" target="_blank" rel="noopener noreferrer">
                随机排序下截止位置k的平均精度：期望与方差
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            Average Precision at Cutoff k under Random Rankings: Expectation and Variance
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tetiana Manzhos, Tetiana Ianevych, Olga Melnyk
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究信息检索中平均精度指标在随机排序下的统计特性，属于传统检索评估的理论分析。虽然涉及搜索领域的评估指标，但论文聚焦于随机排序的统计性质而非实际推荐/搜索系统的核心算法改进，与当前关注的LLM技术、Transformer架构创新或直接应用相关性较弱。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 13:45:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02571v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02571v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">math.PR</span><span class="category-tag">Primary 60E05</span><span class="category-tag">60C05</span><span class="category-tag">Secondary 62R07</span><span class="category-tag">68T05</span></div>
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                    Recommender systems and information retrieval platforms rely on ranking algorithms to present the most relevant items to users, thereby improving engagement and satisfaction. Assessing the quality of these rankings requires reliable evaluation metrics. Among them, Mean Average Precision at cutoff k (MAP@k) is widely used, as it accounts for both the relevance of items and their positions in the list. In this paper, the expectation and variance of Average Precision at k (AP@k) are derived since they can be used as biselines for MAP@k. Here, we covered two widely used evaluation models: offline and online. The expectation establishes the baseline, indicating the level of MAP@k that can be achieved by pure chance. The variance complements this baseline by quantifying the extent of random fluctuations, enabling a more reliable interpretation of observed scores.
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            <a href="https://www.alphaxiv.org/abs/2511.02626v1" target="_blank" rel="noopener noreferrer">
                理解大型语言模型中新知识引发的事实性幻觉：分析、解决方案与解释
            </a>
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            Understanding New-Knowledge-Induced Factual Hallucinations in LLMs: Analysis, Solution, and Interpretation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Renfei Dang, Peng Hu, Changjiang Gao, Shujian Huang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM中的事实性幻觉问题，这属于纯粹的NLP中心话题，与您的核心领域进展无关。虽然幻觉问题在理论上可能与搜索系统中的事实准确性相关，但论文标题明确聚焦于新知识引发的幻觉，缺乏明确的推荐系统、搜索或广告应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 14:55:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02626v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02626v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Previous studies show that introducing new knowledge during large language models (LLMs) fine-tuning can lead to the generation of erroneous output when tested on known information, thereby triggering factual hallucinations. However, existing studies have not deeply investigated the specific manifestations and underlying mechanisms of these hallucinations. Our work addresses this gap by designing a controlled dataset Biography-Reasoning, and conducting a fine-grained analysis across multiple knowledge types and two task types, including knowledge question answering (QA) and knowledge reasoning tasks. We find that when fine-tuned on a dataset in which a specific knowledge type consists entirely of new knowledge, LLMs exhibit significantly increased hallucination tendencies. This suggests that the high unfamiliarity of a particular knowledge type, rather than the overall proportion of new knowledge, is a stronger driver of hallucinations, and these tendencies can even affect other knowledge types in QA tasks. To mitigate such factual hallucinations, we propose KnownPatch, which patches a small number of known knowledge samples in the later stages of training, effectively alleviating new-knowledge-induced hallucinations. Through attention analysis, we find that learning new knowledge reduces the model's attention to key entities in the question, thus causing excessive focus on the surrounding context, which may increase the risk of hallucination. Moreover, the attention pattern can propagate to similar contexts, facilitating the spread of hallucinations to textually similar questions. Our method effectively mitigates the disruption of new knowledge learning to the model's attention on key entities, accompanied by improved performance.
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            <a href="https://www.alphaxiv.org/abs/2511.02623v1" target="_blank" rel="noopener noreferrer">
                对齐问题：当大型语言模型中的正确变为错误
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        <div class="mb-2 text-base text-gray-700">
            The Realignment Problem: When Right becomes Wrong in LLMs
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Aakash Sen Sharma, Debdeep Sanyal, Vivek Srivastava, Shirish Karande, Murari Man...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题关注LLM对齐问题，这主要属于模型安全性和伦理对齐范畴，属于明确的无关主题。虽然对齐技术可能间接影响模型在推荐/搜索中的行为，但论文本身似乎专注于基础对齐问题而非具体应用，与当前关注的推荐系统、搜索广告技术进展或Transformer架构创新缺乏直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 14:52:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02623v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02623v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The alignment of Large Language Models (LLMs) with human values is central to their safe deployment, yet current practice produces static, brittle, and costly-to-maintain models that fail to keep pace with evolving norms and policies. This misalignment, which we term the Alignment-Reality Gap, poses a growing challenge for reliable long-term use. Existing remedies are inadequate: large-scale re-annotation is economically prohibitive, and standard unlearning methods act as blunt instruments that erode utility rather than enable precise policy updates. We introduce TRACE (Triage and Re-align by Alignment Conflict Evaluation), a framework for principled unlearning that reconceives re-alignment as a programmatic policy application problem. TRACE programmatically triages existing preference data against a new policy, identifies high-impact conflicts via a alignment impact score, and applies a hybrid optimization that cleanly inverts, discards, or preserves preferences while safeguarding model performance. Empirical results show that TRACE achieves robust re-alignment across diverse model families (Qwen2.5-7B, Gemma-2-9B, Llama-3.1-8B). On both synthetic benchmarks and the PKU-SafeRLHF dataset under complex policy shift, TRACE enforces new principles without degrading general capabilities. Our work establishes a scalable, dynamic, and cost-effective paradigm for maintaining LLM alignment, providing a foundation for sustainable and responsible AI deployment.
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            <a href="https://www.alphaxiv.org/abs/2511.02599v1" target="_blank" rel="noopener noreferrer">
                下一个令牌知识追踪：利用预训练大语言模型表征解码学生行为
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        <div class="mb-2 text-base text-gray-700">
            Next Token Knowledge Tracing: Exploiting Pretrained LLM Representations to Decode Student Behaviour
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Max Norris, Kobi Gal, Sahan Bulathwela
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于教育领域的知识追踪应用，属于特定领域应用而非核心推荐系统、搜索或广告技术。虽然涉及LLM表征利用，但其应用场景（学生行为解码）与我的关注领域没有直接关联，且没有展示在RecSys/Search/Ads中的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 14:20:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02599v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02599v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Modelling student knowledge is a key challenge when leveraging AI in education, with major implications for personalised learning. The Knowledge Tracing (KT) task aims to predict how students will respond to educational questions in learning environments, based on their prior interactions. Existing KT models typically use response correctness along with metadata like skill tags and timestamps, often overlooking the question text, which is an important source of pedagogical insight. This omission poses a lost opportunity while limiting predictive performance. We propose Next Token Knowledge Tracing (NTKT), a novel approach that reframes KT as a next-token prediction task using pretrained Large Language Models (LLMs). NTKT represents both student histories and question content as sequences of text, allowing LLMs to learn patterns in both behaviour and language. Our series of experiments significantly improves performance over state-of-the-art neural KT models and generalises much better to cold-start questions and users. These findings highlight the importance of question content in KT and demonstrate the benefits of leveraging pretrained representations of LLMs to model student learning more effectively.
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            <a href="https://www.alphaxiv.org/abs/2511.02304v1" target="_blank" rel="noopener noreferrer">
                自动机条件化协作多智能体强化学习
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        <div class="mb-2 text-base text-gray-700">
            Automata-Conditioned Cooperative Multi-Agent Reinforcement Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Beyazit Yalcinkaya, Marcell Vazquez-Chanlatte, Ameesh Shah, Hanna Krasowski, San...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">虽然多智能体强化学习理论上可以应用于推荐系统中的多智能体协作场景，但该论文标题没有明确展示与推荐系统、搜索或广告的直接关联。对于'使能技术'类论文，需要明确说明其在目标领域的潜在应用，而该标题缺乏这种具体性，因此相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 06:37:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02304v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02304v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.MA</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.FL</span><span class="category-tag">cs.LG</span></div>
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                    We study the problem of learning multi-task, multi-agent policies for cooperative, temporal objectives, under centralized training, decentralized execution. In this setting, using automata to represent tasks enables the decomposition of complex tasks into simpler sub-tasks that can be assigned to agents. However, existing approaches remain sample-inefficient and are limited to the single-task case. In this work, we present Automata-Conditioned Cooperative Multi-Agent Reinforcement Learning (ACC-MARL), a framework for learning task-conditioned, decentralized team policies. We identify the main challenges to ACC-MARL's feasibility in practice, propose solutions, and prove the correctness of our approach. We further show that the value functions of learned policies can be used to assign tasks optimally at test time. Experiments show emergent task-aware, multi-step coordination among agents, e.g., pressing a button to unlock a door, holding the door, and short-circuiting tasks.
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            <a href="https://www.alphaxiv.org/abs/2511.02246v1" target="_blank" rel="noopener noreferrer">
                演示：关于LLM偏见和错误的统计显著结果不能保证可泛化的结果
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            Demo: Statistically Significant Results On Biases and Errors of LLMs Do Not Guarantee Generalizable Results
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jonathan Liu, Haoling Qiu, Jonathan Lasko, Damianos Karakos, Mahsa Yarmohammadi,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM的评估、偏见和错误分析，这属于纯粹的NLP评估基准主题，在无关主题列表中明确排除。虽然LLM偏见可能间接影响推荐系统，但论文焦点是评估方法论而非实际应用，没有明确展示在RecSys/Search/Ads中的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 04:20:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02246v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02246v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.HC</span><span class="category-tag">cs.LG</span></div>
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                    Recent research has shown that hallucinations, omissions, and biases are prevalent in everyday use-cases of LLMs. However, chatbots used in medical contexts must provide consistent advice in situations where non-medical factors are involved, such as when demographic information is present. In order to understand the conditions under which medical chatbots fail to perform as expected, we develop an infrastructure that 1) automatically generates queries to probe LLMs and 2) evaluates answers to these queries using multiple LLM-as-a-judge setups and prompts. For 1), our prompt creation pipeline samples the space of patient demographics, histories, disorders, and writing styles to create realistic questions that we subsequently use to prompt LLMs. In 2), our evaluation pipeline provides hallucination and omission detection using LLM-as-a-judge as well as agentic workflows, in addition to LLM-as-a-judge treatment category detectors. As a baseline study, we perform two case studies on inter-LLM agreement and the impact of varying the answering and evaluation LLMs. We find that LLM annotators exhibit low agreement scores (average Cohen's Kappa $\kappa=0.118$), and only specific (answering, evaluation) LLM pairs yield statistically significant differences across writing styles, genders, and races. We recommend that studies using LLM evaluation use multiple LLMs as evaluators in order to avoid arriving at statistically significant but non-generalizable results, particularly in the absence of ground-truth data. We also suggest publishing inter-LLM agreement metrics for transparency. Our code and dataset are available here: https://github.com/BBN-E/medic-neurips-2025-demo.
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            <a href="https://www.alphaxiv.org/abs/2511.02234v1" target="_blank" rel="noopener noreferrer">
                交错指令调优对音频多模态大语言模型语义推理性能的评估
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            An Evaluation of Interleaved Instruction Tuning on Semantic Reasoning Performance in an Audio MLLM
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiawei Liu, Enis Berk Çoban, Zarina Schevchenko, Hao Tang, Zhigang Zhu, Michael ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注音频多模态大语言模型的语义推理性能评估，属于纯粹的评估基准研究。虽然涉及多模态技术，但音频模态与搜索、推荐、广告领域的核心数据模态（文本、图像、用户行为序列）关联性较弱，且论文重点在于评估而非核心架构或应用创新，对当前关注领域的技术推进价值有限。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 03:54:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02234v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02234v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.MM</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.SD</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Standard training for Multi-modal Large Language Models (MLLMs) involves concatenating non-textual information, like vision or audio, with a text prompt. This approach may not encourage deep integration of modalities, limiting the model's ability to leverage the core language model's reasoning capabilities. This work examined the impact of interleaved instruction tuning in an audio MLLM, where audio tokens are interleaved within the prompt. Using the Listen, Think, and Understand (LTU) model as a testbed, we conduct an experiment using the Synonym and Hypernym Audio Reasoning Dataset (SHARD), our newly created reasoning benchmark for audio-based semantic reasoning focusing on synonym and hypernym recognition. Our findings show that while even zero-shot interleaved prompting improves performance on our reasoning tasks, a small amount of fine-tuning using interleaved training prompts improves the results further, however, at the expense of the MLLM's audio labeling ability.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02767v1" target="_blank" rel="noopener noreferrer">
                动态反射：通过文本对齐探测视频表征
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Dynamic Reflections: Probing Video Representations with Text Alignment
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tyler Zhu, Tengda Han, Leonidas Guibas, Viorica Pătrăucean, Maks Ovsjanikov
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频表征与文本对齐的探测方法，这属于纯视觉和多模态研究领域。虽然涉及文本对齐，但核心焦点是视频表征分析，与推荐系统、搜索或广告中的异构数据处理没有直接关联。该技术可能对视频内容理解有帮助，但缺乏明确的推荐/搜索/广告应用场景。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 17:52:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02767v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02767v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The alignment of representations from different modalities has recently been shown to provide insights on the structural similarities and downstream capabilities of different encoders across diverse data types. While significant progress has been made in aligning images with text, the temporal nature of video data remains largely unexplored in this context. In this work, we conduct the first comprehensive study of video-text representation alignment, probing the capabilities of modern video and language encoders. Our findings reveal several key insights. First, we demonstrate that cross-modal alignment highly depends on the richness of both visual (static images vs. multi-frame videos) and text (single caption vs. a collection) data provided at test time, especially when using state-of-the-art video encoders. We propose parametric test-time scaling laws that capture this behavior and show remarkable predictive power against empirical observations. Secondly, we investigate the correlation between semantic alignment and performance on both semantic and non-semantic downstream tasks, providing initial evidence that strong alignment against text encoders may be linked to general-purpose video representation and understanding. Finally, we correlate temporal reasoning with cross-modal alignment providing a challenging test-bed for vision and language models. Overall, our work introduces video-text alignment as an informative zero-shot way to probe the representation power of different encoders for spatio-temporal data. Project page can be found at https://video-prh.github.io/
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            <a href="https://www.alphaxiv.org/abs/2511.02685v1" target="_blank" rel="noopener noreferrer">
                可见光-红外行人重识别的模态转换表示学习
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Modality-Transition Representation Learning for Visible-Infrared Person Re-Identification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chao Yuan, Zanwu Liu, Guiwei Zhang, Haoxuan Xu, Yujian Zhao, Guanglin Niu, Bo Li
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的跨模态行人重识别任务，虽然涉及多模态学习概念，但主要应用于安全监控领域，与推荐系统、搜索或广告的核心技术关联性较弱。可见光-红外模态转换的技术思路可能为处理异构数据提供启发，但缺乏明确的RecSys/Search/Ads应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 16:09:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02685v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02685v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Visible-infrared person re-identification (VI-ReID) technique could associate the pedestrian images across visible and infrared modalities in the practical scenarios of background illumination changes. However, a substantial gap inherently exists between these two modalities. Besides, existing methods primarily rely on intermediate representations to align cross-modal features of the same person. The intermediate feature representations are usually create by generating intermediate images (kind of data enhancement), or fusing intermediate features (more parameters, lack of interpretability), and they do not make good use of the intermediate features. Thus, we propose a novel VI-ReID framework via Modality-Transition Representation Learning (MTRL) with a middle generated image as a transmitter from visible to infrared modals, which are fully aligned with the original visible images and similar to the infrared modality. After that, using a modality-transition contrastive loss and a modality-query regularization loss for training, which could align the cross-modal features more effectively. Notably, our proposed framework does not need any additional parameters, which achieves the same inference speed to the backbone while improving its performance on VI-ReID task. Extensive experimental results illustrate that our model significantly and consistently outperforms existing SOTAs on three typical VI-ReID datasets.
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            <a href="https://www.alphaxiv.org/abs/2511.02576v1" target="_blank" rel="noopener noreferrer">
                基于轻量反馈弱监督的资源高效自动分割细化
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Resource-efficient Automatic Refinement of Segmentations via Weak Supervision from Light Feedback
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Alix de Langlais, Benjamin Billot, Théo Aguilar Vidal, Marc-Olivier Gauci, Hervé...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的分割任务优化和弱监督学习，属于纯粹的视觉技术范畴。虽然提到了资源效率和自动细化，但没有明确与推荐系统、搜索或广告领域的潜在应用连接，也不涉及LLM或Transformer架构的进步。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 13:53:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02576v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02576v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">eess.IV</span><span class="category-tag">cs.CV</span></div>
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                    Delineating anatomical regions is a key task in medical image analysis. Manual segmentation achieves high accuracy but is labor-intensive and prone to variability, thus prompting the development of automated approaches. Recently, a breadth of foundation models has enabled automated segmentations across diverse anatomies and imaging modalities, but these may not always meet the clinical accuracy standards. While segmentation refinement strategies can improve performance, current methods depend on heavy user interactions or require fully supervised segmentations for training. Here, we present SCORE (Segmentation COrrection from Regional Evaluations), a weakly supervised framework that learns to refine mask predictions only using light feedback during training. Specifically, instead of relying on dense training image annotations, SCORE introduces a novel loss that leverages region-wise quality scores and over/under-segmentation error labels. We demonstrate SCORE on humerus CT scans, where it considerably improves initial predictions from TotalSegmentator, and achieves performance on par with existing refinement methods, while greatly reducing their supervision requirements and annotation time. Our code is available at: https://gitlab.inria.fr/adelangl/SCORE.
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            <a href="https://www.alphaxiv.org/abs/2511.02564v1" target="_blank" rel="noopener noreferrer">
                跨时间与视角的视觉理解：基于多时序跨视角学习的鲁棒视频行人重识别
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            Seeing Across Time and Views: Multi-Temporal Cross-View Learning for Robust Video Person Re-Identification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Md Rashidunnabi, Kailash A. Hambarde, Vasco Lopes, Joao C. Neves, Hugo Proenca
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的行人重识别技术，属于纯粹的视觉任务，与推荐系统、搜索或广告的核心技术栈没有直接关联。虽然多时序和跨视角学习技术在某些广义上可能涉及序列建模，但论文的应用场景和核心方法都局限于视觉识别领域，缺乏明确的RecSys/Search/Ads应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 13:37:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02564v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02564v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Video-based person re-identification (ReID) in cross-view domains (for example, aerial-ground surveillance) remains an open problem because of extreme viewpoint shifts, scale disparities, and temporal inconsistencies. To address these challenges, we propose MTF-CVReID, a parameter-efficient framework that introduces seven complementary modules over a ViT-B/16 backbone. Specifically, we include: (1) Cross-Stream Feature Normalization (CSFN) to correct camera and view biases; (2) Multi-Resolution Feature Harmonization (MRFH) for scale stabilization across altitudes; (3) Identity-Aware Memory Module (IAMM) to reinforce persistent identity traits; (4) Temporal Dynamics Modeling (TDM) for motion-aware short-term temporal encoding; (5) Inter-View Feature Alignment (IVFA) for perspective-invariant representation alignment; (6) Hierarchical Temporal Pattern Learning (HTPL) to capture multi-scale temporal regularities; and (7) Multi-View Identity Consistency Learning (MVICL) that enforces cross-view identity coherence using a contrastive learning paradigm. Despite adding only about 2 million parameters and 0.7 GFLOPs over the baseline, MTF-CVReID maintains real-time efficiency (189 FPS) and achieves state-of-the-art performance on the AG-VPReID benchmark across all altitude levels, with strong cross-dataset generalization to G2A-VReID and MARS datasets. These results show that carefully designed adapter-based modules can substantially enhance cross-view robustness and temporal consistency without compromising computational efficiency. The source code is available at https://github.com/MdRashidunnabi/MTF-CVReID
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            <a href="https://www.alphaxiv.org/abs/2511.02563v1" target="_blank" rel="noopener noreferrer">
                城市视觉黑客马拉松数据集与模型：面向印度交通场景的图像标注与精确视觉模型
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            The Urban Vision Hackathon Dataset and Models: Towards Image Annotations and Accurate Vision Models for Indian Traffic
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Akash Sharma, Chinmay Mhatre, Sankalp Gawali, Ruthvik Bokkasam, Brij Kishore, Vi...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域，特别是针对印度交通场景的图像标注和视觉模型开发。虽然视觉技术在某些推荐系统中可能有间接应用，但该论文专注于特定地理区域的交通场景，与搜索、推荐或广告的核心技术领域关联性较弱，且未涉及LLM或Transformer架构的进展。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 13:36:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02563v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02563v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This report describes the UVH-26 dataset, the first public release by AIM@IISc of a large-scale dataset of annotated traffic-camera images from India. The dataset comprises 26,646 high-resolution (1080p) images sampled from 2800 Bengaluru's Safe-City CCTV cameras over a 4-week period, and subsequently annotated through a crowdsourced hackathon involving 565 college students from across India. In total, 1.8 million bounding boxes were labeled across 14 vehicle classes specific to India: Cycle, 2-Wheeler (Motorcycle), 3-Wheeler (Auto-rickshaw), LCV (Light Commercial Vehicles), Van, Tempo-traveller, Hatchback, Sedan, SUV, MUV, Mini-bus, Bus, Truck and Other. Of these, 283k-316k consensus ground truth bounding boxes and labels were derived for distinct objects in the 26k images using Majority Voting and STAPLE algorithms. Further, we train multiple contemporary detectors, including YOLO11-S/X, RT-DETR-S/X, and DAMO-YOLO-T/L using these datasets, and report accuracy based on mAP50, mAP75 and mAP50:95. Models trained on UVH-26 achieve 8.4-31.5% improvements in mAP50:95 over equivalent baseline models trained on COCO dataset, with RT-DETR-X showing the best performance at 0.67 (mAP50:95) as compared to 0.40 for COCO-trained weights for common classes (Car, Bus, and Truck). This demonstrates the benefits of domain-specific training data for Indian traffic scenarios. The release package provides the 26k images with consensus annotations based on Majority Voting (UVH-26-MV) and STAPLE (UVH-26-ST) and the 6 fine-tuned YOLO and DETR models on each of these datasets. By capturing the heterogeneity of Indian urban mobility directly from operational traffic-camera streams, UVH-26 addresses a critical gap in existing global benchmarks, and offers a foundation for advancing detection, classification, and deployment of intelligent transportation systems in emerging nations with complex traffic conditions.
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            <a href="https://www.alphaxiv.org/abs/2511.02510v1" target="_blank" rel="noopener noreferrer">
                LiteVoxel：用于高效体素栅格化的低内存智能阈值技术
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            LiteVoxel: Low-memory Intelligent Thresholding for Efficient Voxel Rasterization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jee Won Lee, Jongseong Brad Choi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域的体素栅格化效率优化，属于3D视觉技术范畴。虽然提到了低内存和高效处理，但没有明确展示与推荐系统、搜索或广告的潜在应用连接，且属于被排除的'纯视觉'类别。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 11:55:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02510v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02510v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Sparse-voxel rasterization is a fast, differentiable alternative for optimization-based scene reconstruction, but it tends to underfit low-frequency content, depends on brittle pruning heuristics, and can overgrow in ways that inflate VRAM. We introduce LiteVoxel, a self-tuning training pipeline that makes SV rasterization both steadier and lighter. Our loss is made low-frequency aware via an inverse-Sobel reweighting with a mid-training gamma-ramp, shifting gradient budget to flat regions only after geometry stabilize. Adaptation replaces fixed thresholds with a depth-quantile pruning logic on maximum blending weight, stabilized by EMA-hysteresis guards and refines structure through ray-footprint-based, priority-driven subdivision under an explicit growth budget. Ablations and full-system results across Mip-NeRF 360 (6scenes) and Tanks & Temples (3scenes) datasets show mitigation of errors in low-frequency regions and boundary instability while keeping PSNR/SSIM, training time, and FPS comparable to a strong SVRaster pipeline. Crucially, LiteVoxel reduces peak VRAM by ~40%-60% and preserves low-frequency detail that prior setups miss, enabling more predictable, memory-efficient training without sacrificing perceptual quality.
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            <a href="https://www.alphaxiv.org/abs/2511.02483v1" target="_blank" rel="noopener noreferrer">
                OLATverse：一个具有精确光照控制的大规模真实世界物体数据集
            </a>
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        <div class="mb-2 text-base text-gray-700">
            OLATverse: A Large-scale Real-world Object Dataset with Precise Lighting Control
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xilong Zhou, Jianchun Chen, Pramod Rao, Timo Teufel, Linjie Lyu, Tigran Minasian...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域的大规模物体数据集构建和光照控制技术，属于纯粹的视觉数据收集工作。虽然视觉数据在推荐和广告中可能有间接应用，但该论文本身没有涉及任何推荐系统、搜索、广告或LLM相关技术，也没有展示如何将这些视觉数据应用于相关领域。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 11:13:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02483v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02483v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.GR</span></div>
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                    We introduce OLATverse, a large-scale dataset comprising around 9M images of 765 real-world objects, captured from multiple viewpoints under a diverse set of precisely controlled lighting conditions. While recent advances in object-centric inverse rendering, novel view synthesis and relighting have shown promising results, most techniques still heavily rely on the synthetic datasets for training and small-scale real-world datasets for benchmarking, which limits their realism and generalization. To address this gap, OLATverse offers two key advantages over existing datasets: large-scale coverage of real objects and high-fidelity appearance under precisely controlled illuminations. Specifically, OLATverse contains 765 common and uncommon real-world objects, spanning a wide range of material categories. Each object is captured using 35 DSLR cameras and 331 individually controlled light sources, enabling the simulation of diverse illumination conditions. In addition, for each object, we provide well-calibrated camera parameters, accurate object masks, photometric surface normals, and diffuse albedo as auxiliary resources. We also construct an extensive evaluation set, establishing the first comprehensive real-world object-centric benchmark for inverse rendering and normal estimation. We believe that OLATverse represents a pivotal step toward integrating the next generation of inverse rendering and relighting methods with real-world data. The full dataset, along with all post-processing workflows, will be publicly released at https://vcai.mpi-inf.mpg.de/projects/OLATverse/.
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            <a href="https://www.alphaxiv.org/abs/2511.02473v1" target="_blank" rel="noopener noreferrer">
                MVAFormer：基于RGB的多视角时空动作识别Transformer模型
            </a>
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            MVAFormer: RGB-based Multi-View Spatio-Temporal Action Recognition with Transformer
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Taiga Yamane, Satoshi Suzuki, Ryo Masumura, Shotaro Tora
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的多视角时空动作识别，虽然使用了Transformer架构，但其应用场景局限于RGB视频中的动作识别。对于推荐系统、搜索或广告领域，这种纯粹的视觉动作识别技术缺乏明确的直接应用价值，无法有效处理用户行为序列、上下文特征等核心数据模态。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 10:59:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02473v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02473v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Multi-view action recognition aims to recognize human actions using multiple camera views and deals with occlusion caused by obstacles or crowds. In this task, cooperation among views, which generates a joint representation by combining multiple views, is vital. Previous studies have explored promising cooperation methods for improving performance. However, since their methods focus only on the task setting of recognizing a single action from an entire video, they are not applicable to the recently popular spatio-temporal action recognition~(STAR) setting, in which each person's action is recognized sequentially. To address this problem, this paper proposes a multi-view action recognition method for the STAR setting, called MVAFormer. In MVAFormer, we introduce a novel transformer-based cooperation module among views. In contrast to previous studies, which utilize embedding vectors with lost spatial information, our module utilizes the feature map for effective cooperation in the STAR setting, which preserves the spatial information. Furthermore, in our module, we divide the self-attention for the same and different views to model the relationship between multiple views effectively. The results of experiments using a newly collected dataset demonstrate that MVAFormer outperforms the comparison baselines by approximately $4.4$ points on the F-measure.
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            <a href="https://www.alphaxiv.org/abs/2511.02462v1" target="_blank" rel="noopener noreferrer">
                KAO：扩散模型中用于卫星图像的内核自适应优化
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            KAO: Kernel-Adaptive Optimization in Diffusion for Satellite Image
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Teerapong Panboonyuen
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注卫星图像领域的扩散模型优化技术，属于计算机视觉的特定应用领域。虽然内核自适应优化技术本身可能有通用性，但论文明确限定在卫星图像处理，与推荐系统、搜索或广告的核心技术领域没有直接关联，也没有展示在异构数据建模方面的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 10:44:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02462v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02462v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Satellite image inpainting is a crucial task in remote sensing, where accurately restoring missing or occluded regions is essential for robust image analysis. In this paper, we propose KAO, a novel framework that utilizes Kernel-Adaptive Optimization within diffusion models for satellite image inpainting. KAO is specifically designed to address the challenges posed by very high-resolution (VHR) satellite datasets, such as DeepGlobe and the Massachusetts Roads Dataset. Unlike existing methods that rely on preconditioned models requiring extensive retraining or postconditioned models with significant computational overhead, KAO introduces a Latent Space Conditioning approach, optimizing a compact latent space to achieve efficient and accurate inpainting. Furthermore, we incorporate Explicit Propagation into the diffusion process, facilitating forward-backward fusion, which improves the stability and precision of the method. Experimental results demonstrate that KAO sets a new benchmark for VHR satellite image restoration, providing a scalable, high-performance solution that balances the efficiency of preconditioned models with the flexibility of postconditioned models.
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            <a href="https://www.alphaxiv.org/abs/2511.02427v1" target="_blank" rel="noopener noreferrer">
                从实验室到现实应用：评估移动机器人边缘设备上的零样本场景理解
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            From the Laboratory to Real-World Application: Evaluating Zero-Shot Scene Interpretation on Edge Devices for Mobile Robotics
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nicolas Schuler, Lea Dewald, Nick Baldig, Jürgen Graf
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注移动机器人的边缘计算和零样本场景理解，这与推荐系统、搜索或广告的核心领域没有直接关联。虽然边缘计算技术可能间接应用于移动推荐场景，但论文的机器人应用焦点使其相关性非常有限。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 09:58:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02427v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02427v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                    Video Understanding, Scene Interpretation and Commonsense Reasoning are highly challenging tasks enabling the interpretation of visual information, allowing agents to perceive, interact with and make rational decisions in its environment. Large Language Models (LLMs) and Visual Language Models (VLMs) have shown remarkable advancements in these areas in recent years, enabling domain-specific applications as well as zero-shot open vocabulary tasks, combining multiple domains. However, the required computational complexity poses challenges for their application on edge devices and in the context of Mobile Robotics, especially considering the trade-off between accuracy and inference time. In this paper, we investigate the capabilities of state-of-the-art VLMs for the task of Scene Interpretation and Action Recognition, with special regard to small VLMs capable of being deployed to edge devices in the context of Mobile Robotics. The proposed pipeline is evaluated on a diverse dataset consisting of various real-world cityscape, on-campus and indoor scenarios. The experimental evaluation discusses the potential of these small models on edge devices, with particular emphasis on challenges, weaknesses, inherent model biases and the application of the gained information. Supplementary material is provided via the following repository: https://datahub.rz.rptu.de/hstr-csrl-public/publications/scene-interpretation-on-edge-devices/
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            <a href="https://www.alphaxiv.org/abs/2511.02426v1" target="_blank" rel="noopener noreferrer">
                基于Kullback-Leibler散度的输入-系统状态识别方法
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            A Kullback-Leibler divergence method for input-system-state identification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Marios Impraimakis
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于系统状态识别和Kullback-Leibler散度方法，属于通用的统计推断和系统识别领域。虽然KL散度在推荐系统中可用于衡量分布差异（如用户兴趣分布），但论文标题未表明与推荐系统、搜索或广告的直接关联，也未涉及LLM、Transformer架构或多模态建模等核心技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 09:57:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02426v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02426v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">eess.SP</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.IT</span><span class="category-tag">cs.SY</span><span class="category-tag">eess.SY</span><span class="category-tag">math.IT</span><span class="category-tag">68T05 (Learning and adaptive systems)</span><span class="category-tag">I.2.6; I.2.8</span></div>
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                    The capability of a novel Kullback-Leibler divergence method is examined herein within the Kalman filter framework to select the input-parameter-state estimation execution with the most plausible results. This identification suffers from the uncertainty related to obtaining different results from different initial parameter set guesses, and the examined approach uses the information gained from the data in going from the prior to the posterior distribution to address the issue. Firstly, the Kalman filter is performed for a number of different initial parameter sets providing the system input-parameter-state estimation. Secondly, the resulting posterior distributions are compared simultaneously to the initial prior distributions using the Kullback-Leibler divergence. Finally, the identification with the least Kullback-Leibler divergence is selected as the one with the most plausible results. Importantly, the method is shown to select the better performed identification in linear, nonlinear, and limited information applications, providing a powerful tool for system monitoring.
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            <a href="https://www.alphaxiv.org/abs/2511.02415v1" target="_blank" rel="noopener noreferrer">
                ChartM³：一个用于图表理解中构建多维多步视觉推理数据的多阶段代码驱动流水线
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            ChartM$^3$: A Multi-Stage Code-Driven Pipeline for Constructing Multi-Dimensional and Multi-Step Visual Reasoning Data in Chart Comprehension
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Duo Xu, Hao Cheng, Xin Lin, Zhen Xie, Hao Wang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注图表视觉推理数据的构建方法，属于计算机视觉和视觉语言模型领域。虽然提到了多模态数据处理，但其核心是图表视觉理解而非推荐系统、搜索或广告中的异构数据建模。该技术可能间接启发多模态特征处理，但缺乏明确的RecSys/Search/Ads应用潜力。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 09:45:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02415v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02415v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Complex chart understanding tasks demand advanced visual recognition and reasoning capabilities from multimodal large language models (MLLMs). However, current research provides limited coverage of complex chart scenarios and computation-intensive reasoning tasks prevalent in real-world applications. This study proposes an automated multi-stage code-driven pipeline for systematically generating visual reasoning datasets to address these limitations. The pipeline integrates retrieval-augmented generation (RAG) to retrieve professional chart templates and employs chain-of-thought (CoT) strategies to generate reasoning codes that simulate real data distributions, thereby driving chart rendering and question-related statistical computations. Through model-based evaluation, the pipeline enhances chart diversity and data quality. Using this framework, we construct ChartM$^3$, a multi-dimensional and multi-step dataset containing 38K charts and 142K Q&A pairs for training, along with 2,871 high-quality evaluation samples for enabling practical performance assessment. Supervised fine-tuning (SFT) and reinforcement learning (RL) experiments demonstrate that our dataset significantly improves reasoning capabilities and cross-domain generalization performance, enabling smaller models to achieve performance comparable to larger-scale models in complex chart comprehension.
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            <a href="https://www.alphaxiv.org/abs/2511.02395v1" target="_blank" rel="noopener noreferrer">
                稀疏噪声雷达点云的自监督运动目标分割
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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            Self-Supervised Moving Object Segmentation of Sparse and Noisy Radar Point Clouds
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Leon Schwarzer, Matthias Zeller, Daniel Casado Herraez, Simon Dierl, Michael Hei...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于雷达点云的运动目标分割，属于计算机视觉和自动驾驶领域。虽然涉及稀疏数据处理，但缺乏与推荐系统、搜索或广告的直接关联。雷达点云分割技术可能在某些边缘场景下用于位置感知，但这种应用过于间接且非核心。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 09:21:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02395v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02395v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Moving object segmentation is a crucial task for safe and reliable autonomous mobile systems like self-driving cars, improving the reliability and robustness of subsequent tasks like SLAM or path planning. While the segmentation of camera or LiDAR data is widely researched and achieves great results, it often introduces an increased latency by requiring the accumulation of temporal sequences to gain the necessary temporal context. Radar sensors overcome this problem with their ability to provide a direct measurement of a point's Doppler velocity, which can be exploited for single-scan moving object segmentation. However, radar point clouds are often sparse and noisy, making data annotation for use in supervised learning very tedious, time-consuming, and cost-intensive. To overcome this problem, we address the task of self-supervised moving object segmentation of sparse and noisy radar point clouds. We follow a two-step approach of contrastive self-supervised representation learning with subsequent supervised fine-tuning using limited amounts of annotated data. We propose a novel clustering-based contrastive loss function with cluster refinement based on dynamic points removal to pretrain the network to produce motion-aware representations of the radar data. Our method improves label efficiency after fine-tuning, effectively boosting state-of-the-art performance by self-supervised pretraining.
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            <a href="https://www.alphaxiv.org/abs/2511.02335v1" target="_blank" rel="noopener noreferrer">
                GAFD-CC：面向分布外检测的全局感知特征解耦与置信度校准
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            <i class="fa fa-star mr-1"></i>2/10
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            GAFD-CC: Global-Aware Feature Decoupling with Confidence Calibration for OOD Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kun Zou, Yongheng Xu, Jianxing Yu, Yan Pan, Jian Yin, Hanjiang Lai
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注分布外检测和置信度校准，属于通用机器学习可靠性领域，与推荐系统、搜索或广告的核心技术关联较弱。虽然置信度校准在模型部署中有一定价值，但论文未明确展示其在RecSys/Search/Ads领域的直接应用潜力，且不涉及LLM、Transformer架构或异构数据建模等核心技术方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 07:40:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02335v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02335v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Out-of-distribution (OOD) detection is paramount to ensuring the reliability and robustness of learning models in real-world applications. Existing post-hoc OOD detection methods detect OOD samples by leveraging their features and logits information without retraining. However, they often overlook the inherent correlation between features and logits, which is crucial for effective OOD detection. To address this limitation, we propose Global-Aware Feature Decoupling with Confidence Calibration (GAFD-CC). GAFD-CC aims to refine decision boundaries and increase discriminative performance. Firstly, it performs global-aware feature decoupling guided by classification weights. This involves aligning features with the direction of global classification weights to decouple them. From this, GAFD-CC extracts two types of critical information: positively correlated features that promote in-distribution (ID)/OOD boundary refinement and negatively correlated features that suppress false positives and tighten these boundaries. Secondly, it adaptively fuses these decoupled features with multi-scale logit-based confidence for comprehensive and robust OOD detection. Extensive experiments on large-scale benchmarks demonstrate GAFD-CC's competitive performance and strong generalization ability compared to those of state-of-the-art methods.
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            <a href="https://www.alphaxiv.org/abs/2511.02215v1" target="_blank" rel="noopener noreferrer">
                基础模型能否彻底改变移动增强现实的稀疏感知？
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Can Foundation Models Revolutionize Mobile AR Sparse Sensing?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yiqin Zhao, Tian Guo
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注移动增强现实中的稀疏感知问题，这属于计算机视觉和移动计算领域，与推荐系统、搜索或广告的核心技术栈关联度较低。虽然基础模型技术本身具有广泛适用性，但论文聚焦的AR稀疏感知应用场景与RecSys/Search/Ads的核心业务需求（如用户行为建模、内容匹配、排序优化等）缺乏直接的技术迁移路径。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 03:06:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02215v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02215v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.ET</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Mobile sensing systems have long faced a fundamental trade-off between sensing quality and efficiency due to constraints in computation, power, and other limitations. Sparse sensing, which aims to acquire and process only a subset of sensor data, has been a key strategy for maintaining performance under such constraints. However, existing sparse sensing methods often suffer from reduced accuracy, as missing information across space and time introduces uncertainty into many sensing systems. In this work, we investigate whether foundation models can change the landscape of mobile sparse sensing. Using real-world mobile AR data, our evaluations demonstrate that foundation models offer significant improvements in geometry-aware image warping, a central technique for enabling accurate reuse of cross-frame information. Furthermore, our study demonstrates the scalability of foundation model-based sparse sensing and shows its leading performance in 3D scene reconstruction. Collectively, our study reveals critical aspects of the promises and the open challenges of integrating foundation models into mobile sparse sensing systems.
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            <a href="https://www.alphaxiv.org/abs/2511.02296v1" target="_blank" rel="noopener noreferrer">
                图书馆与文化：研究趋势的科学计量分析与可视化
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Library and Culture: A Scientometric Analysis and Visualization of Research Trends
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Auwalu Abdullahi Umar, Muneer Ahmad, Dr M Sadik Batcha
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图书馆学和文化研究的科学计量分析，属于特定领域的研究趋势分析，与推荐系统、搜索、广告或LLM技术没有任何直接或间接关联。论文内容纯粹是学术研究趋势的可视化分析，不涉及任何核心推荐系统技术、Transformer架构或LLM应用。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 06:19:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02296v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02296v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.DL</span><span class="category-tag">cs.IR</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The significance of libraries in preserving and maintaining history and traditional culture cannot be overlooked. It is from this purpose that libraries are to envisage in their programmes cultural activities which must be collected, documented and preserved for posterity. The usefulness of preserved information lies in the fact that the generation to come will be able to establish their identity. This will also assist them with a foundation to build from. This study focus on the growth and development of Library and Culture research in forms of publications reflected in Web of Science database, during the span of 2010-2019. A total 890 publications were found and the highest 124 (13.93%) publications published in 2019.The analysis maps comprehensively the parameters of total output, growth of output, authorship, institution wise and country-level collaboration patterns, major contributors (individuals, top publication sources, institutions, and countries). It exposed that the most prolific author is Lo P secured first place by contributing 4 (0.45%) publications, followed by Bressan V 3 (0.34%) publications in Library and Culture literature. Journal of Academic Librarianship produced the highest number of records 29 (3.26%) followed by Australian Library Journal having contributed 21 (2.36%).It is identified the domination of Wuhan University; School Information Management had contributed 6 (0.67%) of total research output. Authors from USA published the highest number of publications with a total of 244 (27.42%), followed by UK and Australia with 118 (13.26%) and 76 (8.54%) publications were produced respectively.
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            <a href="https://www.alphaxiv.org/abs/2511.02275v1" target="_blank" rel="noopener noreferrer">
                斑秃疾病研究成果：2010年至2019年出版物科学计量分析
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        <div class="mb-2 text-base text-gray-700">
            Research Output on Alopecia Areata Disease: A Scientometric Analysis of Publications from 2010 to 2019
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Muneer Ahmad, M Sadik Batcha
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于医学领域（斑秃疾病）的科学计量分析，属于明确的医学/生物学应用范畴。该主题与推荐系统、搜索、广告或LLM技术没有任何关联，完全落在不相关主题范围内。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 05:26:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02275v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02275v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.DL</span><span class="category-tag">cs.IR</span></div>
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                    The present study is undertaken to find out the publication trends on Alopecia Areata Disease during 2010-2019 from the global perspective. The study mainly focus on distribution of research output, top journals for publications, most prolific authors, authorship pattern, and citations pattern on Alopecia Areata Disease. The results indicate that highest growth rate of publications occurred during the year 2019. Columbia University topped the scene among all institutes. The maximum publications were more than four authored publications. Christiano AM and Clynes R were found to be the most prolific authors. It is also found that most of the prolific authors (by number of publications) do appear in highly cited publications list. Alopecia Areata Disease researchers mostly preferred using article publications to communicate their findings.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02752v1" target="_blank" rel="noopener noreferrer">
                人工智能在低资源语言国家的扩散
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        <div class="mb-2 text-base text-gray-700">
            AI Diffusion in Low Resource Language Countries
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Amit Misra, Syed Waqas Zamir, Wassim Hamidouche, Inbal Becker-Reshef, Juan Lavis...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题关注AI技术在低资源语言国家的扩散和采用，这属于技术部署和社会影响范畴，与推荐系统、搜索或广告的核心技术进展无关。标题中未提及任何与LLM、Transformer架构、推荐系统或搜索广告相关的技术内容，也没有暗示对异构数据建模或效率改进的潜在应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 17:31:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02752v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02752v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CY</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Artificial intelligence (AI) is diffusing globally at unprecedented speed, but adoption remains uneven. Frontier Large Language Models (LLMs) are known to perform poorly on low-resource languages due to data scarcity. We hypothesize that this performance deficit reduces the utility of AI, thereby slowing adoption in Low-Resource Language Countries (LRLCs). To test this, we use a weighted regression model to isolate the language effect from socioeconomic and demographic factors, finding that LRLCs have a share of AI users that is approximately 20% lower relative to their baseline. These results indicate that linguistic accessibility is a significant, independent barrier to equitable AI diffusion.
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            <a href="https://www.alphaxiv.org/abs/2511.02721v1" target="_blank" rel="noopener noreferrer">
                PragExTra：一个多语言语用显化翻译语料库
            </a>
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            PragExTra: A Multilingual Corpus of Pragmatic Explicitation in Translation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Doreen Osmelak, Koel Dutta Chowdhury, Uliana Sentsova, Cristina España-Bonet, Jo...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于翻译领域的语用显化研究，属于纯粹的语言学和机器翻译范畴。标题中没有任何元素表明与推荐系统、搜索、广告或相关使能技术有关，完全超出了当前关注的技术领域范围。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 16:44:57
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                <a href="https://arxiv.org/abs/2511.02721v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02721v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Translators often enrich texts with background details that make implicit cultural meanings explicit for new audiences. This phenomenon, known as pragmatic explicitation, has been widely discussed in translation theory but rarely modeled computationally. We introduce PragExTra, the first multilingual corpus and detection framework for pragmatic explicitation. The corpus covers eight language pairs from TED-Multi and Europarl and includes additions such as entity descriptions, measurement conversions, and translator remarks. We identify candidate explicitation cases through null alignments and refined using active learning with human annotation. Our results show that entity and system-level explicitations are most frequent, and that active learning improves classifier accuracy by 7-8 percentage points, achieving up to 0.88 accuracy and 0.82 F1 across languages. PragExTra establishes pragmatic explicitation as a measurable, cross-linguistic phenomenon and takes a step towards building culturally aware machine translation. Keywords: translation, multilingualism, explicitation
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            <a href="https://www.alphaxiv.org/abs/2511.02687v1" target="_blank" rel="noopener noreferrer">
                协作鸿沟
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            The Collaboration Gap
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tim R. Davidson, Adam Fourney, Saleema Amershi, Robert West, Eric Horvitz, Ece K...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这个标题过于宽泛模糊，没有提供任何具体的技术领域或应用场景信息。从字面意思无法判断是否与推荐系统、搜索、广告或相关使能技术有任何关联，因此评估为完全不相关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 16:10:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02687v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02687v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The trajectory of AI development suggests that we will increasingly rely on agent-based systems composed of independently developed agents with different information, privileges, and tools. The success of these systems will critically depend on effective collaboration among these heterogeneous agents, even under partial observability. Despite intense interest, few empirical studies have evaluated such agent-agent collaboration at scale. We propose a collaborative maze-solving benchmark that (i) isolates collaborative capabilities, (ii) modulates problem complexity, (iii) enables scalable automated grading, and (iv) imposes no output-format constraints, preserving ecological plausibility. Using this framework, we evaluate 32 leading open- and closed-source models in solo, homogeneous, and heterogeneous pairings. Our results reveal a "collaboration gap": models that perform well solo often degrade substantially when required to collaborate. Collaboration can break down dramatically; for instance, small distilled models that solve mazes well alone may fail almost completely in certain pairings. We find that starting with the stronger agent often improves outcomes, motivating a "relay inference" approach where the stronger agent leads before handing off to the weaker one, closing much of the gap. Our findings argue for (1) collaboration-aware evaluation, (2) training strategies developed to enhance collaborative capabilities, and (3) interaction design that reliably elicits agents' latent skills, guidance that applies to AI-AI and human-AI collaboration.
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            <a href="https://www.alphaxiv.org/abs/2511.02587v1" target="_blank" rel="noopener noreferrer">
                英语到罗马尼亚语机器翻译中词汇错误分析
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            The Analysis of Lexical Errors in Machine Translation from English into Romanian
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Angela Stamatie
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于机器翻译中的词汇错误分析，属于纯NLP领域的技术细节研究。论文内容与推荐系统、搜索或广告的核心进展、使能技术或直接应用没有任何关联，也不涉及Transformer架构改进或多模态建模等使能技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 14:07:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02587v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02587v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    The research explores error analysis in the performance of translating by Machine Translation from English into Romanian, and it focuses on lexical errors found in texts which include official information, provided by the World Health Organization (WHO), the Gavi Organization, by the patient information leaflet (the information about the active ingredients of the vaccines or the medication, the indications, the dosage instructions, the storage instructions, the side effects and warning, etc.). All of these texts are related to Covid-19 and have been translated by Google Translate, a multilingual Machine Translation that was created by Google. In the last decades, Google has actively worked to develop a more accurate and fluent automatic translation system. This research, specifically focused on improving Google Translate, aims to enhance the overall quality of Machine Translation by achieving better lexical selection and by reducing errors. The investigation involves a comprehensive analysis of 230 texts that have been translated from English into Romanian.
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            <a href="https://www.alphaxiv.org/abs/2511.02495v1" target="_blank" rel="noopener noreferrer">
                DetectiumFire：一个连接视觉与语言用于火灾理解的综合性多模态数据集
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            DetectiumFire: A Comprehensive Multi-modal Dataset Bridging Vision and Language for Fire Understanding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zixuan Liu, Siavash H. Khajavi, Guangkai Jiang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于火灾理解的多模态数据集构建，属于纯粹的视觉-语言模态研究。虽然涉及多模态技术，但其应用领域（火灾理解）与推荐系统、搜索或广告完全无关，且没有显示出任何在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 11:33:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02495v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02495v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advances in multi-modal models have demonstrated strong performance in tasks such as image generation and reasoning. However, applying these models to the fire domain remains challenging due to the lack of publicly available datasets with high-quality fire domain annotations. To address this gap, we introduce DetectiumFire, a large-scale, multi-modal dataset comprising of 22.5k high-resolution fire-related images and 2.5k real-world fire-related videos covering a wide range of fire types, environments, and risk levels. The data are annotated with both traditional computer vision labels (e.g., bounding boxes) and detailed textual prompts describing the scene, enabling applications such as synthetic data generation and fire risk reasoning. DetectiumFire offers clear advantages over existing benchmarks in scale, diversity, and data quality, significantly reducing redundancy and enhancing coverage of real-world scenarios. We validate the utility of DetectiumFire across multiple tasks, including object detection, diffusion-based image generation, and vision-language reasoning. Our results highlight the potential of this dataset to advance fire-related research and support the development of intelligent safety systems. We release DetectiumFire to promote broader exploration of fire understanding in the AI community. The dataset is available at https://kaggle.com/datasets/38b79c344bdfc55d1eed3d22fbaa9c31fad45e27edbbe9e3c529d6e5c4f93890
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            <a href="https://www.alphaxiv.org/abs/2511.02458v1" target="_blank" rel="noopener noreferrer">
                基于提示的政策分析：利用合成LLM角色预测宏观经济情景
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        <div class="mb-2 text-base text-gray-700">
            Prompting for Policy: Forecasting Macroeconomic Scenarios with Synthetic LLM Personas
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Giulia Iadisernia, Carolina Camassa
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于使用LLM进行宏观经济情景预测和政策分析，这属于经济学和金融领域的特定应用，与推荐系统、搜索或广告的核心技术进展无关。论文内容涉及宏观经济建模和政策模拟，没有显示出在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 10:38:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02458v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02458v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.CE</span><span class="category-tag">econ.GN</span><span class="category-tag">q-fin.EC</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We evaluate whether persona-based prompting improves Large Language Model (LLM) performance on macroeconomic forecasting tasks. Using 2,368 economics-related personas from the PersonaHub corpus, we prompt GPT-4o to replicate the ECB Survey of Professional Forecasters across 50 quarterly rounds (2013-2025). We compare the persona-prompted forecasts against the human experts panel, across four target variables (HICP, core HICP, GDP growth, unemployment) and four forecast horizons. We also compare the results against 100 baseline forecasts without persona descriptions to isolate its effect. We report two main findings. Firstly, GPT-4o and human forecasters achieve remarkably similar accuracy levels, with differences that are statistically significant yet practically modest. Our out-of-sample evaluation on 2024-2025 data demonstrates that GPT-4o can maintain competitive forecasting performance on unseen events, though with notable differences compared to the in-sample period. Secondly, our ablation experiment reveals no measurable forecasting advantage from persona descriptions, suggesting these prompt components can be omitted to reduce computational costs without sacrificing accuracy. Our results provide evidence that GPT-4o can achieve competitive forecasting accuracy even on out-of-sample macroeconomic events, if provided with relevant context data, while revealing that diverse prompts produce remarkably homogeneous forecasts compared to human panels.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02376v1" target="_blank" rel="noopener noreferrer">
                AutoAdv：面向大型语言模型多轮越狱的自动化对抗性提示生成
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
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        <div class="mb-2 text-base text-gray-700">
            AutoAdv: Automated Adversarial Prompting for Multi-Turn Jailbreaking of Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Aashray Reddy, Andrew Zagula, Nicholas Saban
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM安全性和对抗性攻击（越狱），这属于纯粹的LLM安全研究范畴，与我的关注领域（推荐系统、搜索、广告中的核心进展、使能技术及应用）无关。论文内容涉及对抗性提示和模型安全漏洞，没有展示在RecSys/Search/Ads中的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 08:56:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02376v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02376v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CR</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) remain vulnerable to jailbreaking attacks where adversarial prompts elicit harmful outputs, yet most evaluations focus on single-turn interactions while real-world attacks unfold through adaptive multi-turn conversations. We present AutoAdv, a training-free framework for automated multi-turn jailbreaking that achieves up to 95% attack success rate on Llama-3.1-8B within six turns a 24 percent improvement over single turn baselines. AutoAdv uniquely combines three adaptive mechanisms: a pattern manager that learns from successful attacks to enhance future prompts, a temperature manager that dynamically adjusts sampling parameters based on failure modes, and a two-phase rewriting strategy that disguises harmful requests then iteratively refines them. Extensive evaluation across commercial and open-source models (GPT-4o-mini, Qwen3-235B, Mistral-7B) reveals persistent vulnerabilities in current safety mechanisms, with multi-turn attacks consistently outperforming single-turn approaches. These findings demonstrate that alignment strategies optimized for single-turn interactions fail to maintain robustness across extended conversations, highlighting an urgent need for multi-turn-aware defenses.
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            <a href="https://www.alphaxiv.org/abs/2511.02374v1" target="_blank" rel="noopener noreferrer">
                AyurParam：面向阿育吠陀医学领域的最先进双语语言模型
            </a>
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            AyurParam: A State-of-the-Art Bilingual Language Model for Ayurveda
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mohd Nauman, Sravan Gvm, Vijay Devane, Shyam Pawar, Viraj Thakur, Kundeshwar Pun...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于阿育吠陀医学领域的双语语言模型开发，这属于医学/生物学特定领域的应用。虽然涉及语言模型技术，但论文主题明确限定在医学领域，与推荐系统、搜索或广告的核心关注点没有直接关联，也不具备在这些领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 08:53:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02374v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02374v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Current large language models excel at broad, general-purpose tasks, but consistently underperform when exposed to highly specialized domains that require deep cultural, linguistic, and subject-matter expertise. In particular, traditional medical systems such as Ayurveda embody centuries of nuanced textual and clinical knowledge that mainstream LLMs fail to accurately interpret or apply. We introduce AyurParam-2.9B, a domain-specialized, bilingual language model fine-tuned from Param-1-2.9B using an extensive, expertly curated Ayurveda dataset spanning classical texts and clinical guidance. AyurParam's dataset incorporates context-aware, reasoning, and objective-style Q&A in both English and Hindi, with rigorous annotation protocols for factual precision and instructional clarity. Benchmarked on BhashaBench-Ayur, AyurParam not only surpasses all open-source instruction-tuned models in its size class (1.5--3B parameters), but also demonstrates competitive or superior performance compared to much larger models. The results from AyurParam highlight the necessity for authentic domain adaptation and high-quality supervision in delivering reliable, culturally congruent AI for specialized medical knowledge.
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            <a href="https://www.alphaxiv.org/abs/2511.02366v1" target="_blank" rel="noopener noreferrer">
                LiveSecBench：面向中文语境下大语言模型的动态与文化相关AI安全基准
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            LiveSecBench: A Dynamic and Culturally-Relevant AI Safety Benchmark for LLMs in Chinese Context
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yudong Li, Zhongliang Yang, Kejiang Chen, Wenxuan Wang, Tianxin Zhang, Sifang Wa...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于AI安全基准测试，属于评估基准范畴，这在无关主题中被明确排除。虽然涉及中文语境，但核心关注点是安全评估而非推荐系统、搜索或广告的技术进步。该工作没有展示在推荐系统、搜索或广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 08:44:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02366v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02366v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    In this work, we propose LiveSecBench, a dynamic and continuously updated safety benchmark specifically for Chinese-language LLM application scenarios. LiveSecBench evaluates models across six critical dimensions (Legality, Ethics, Factuality, Privacy, Adversarial Robustness, and Reasoning Safety) rooted in the Chinese legal and social frameworks. This benchmark maintains relevance through a dynamic update schedule that incorporates new threat vectors, such as the planned inclusion of Text-to-Image Generation Safety and Agentic Safety in the next update. For now, LiveSecBench (v251030) has evaluated 18 LLMs, providing a landscape of AI safety in the context of Chinese language. The leaderboard is publicly accessible at https://livesecbench.intokentech.cn/.
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            <a href="https://www.alphaxiv.org/abs/2511.02347v1" target="_blank" rel="noopener noreferrer">
                LTD-Bench：通过让大语言模型绘画来评估它们
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            LTD-Bench: Evaluating Large Language Models by Letting Them Draw
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Liuhao Lin, Ke Li, Zihan Xu, Yuchen Shi, Yulei Qin, Yan Zhang, Xing Sun, Rongron...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确表明这是一个关于评估大语言模型能力的基准测试，专注于绘画生成任务。这属于纯粹的LLM评估基准范畴，与我的关注点中的核心领域进展、赋能技术或直接应用完全无关，且明确排除了幻觉、评估基准等纯NLP中心话题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 08:11:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02347v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02347v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Current evaluation paradigms for large language models (LLMs) represent a critical blind spot in AI research--relying on opaque numerical metrics that conceal fundamental limitations in spatial reasoning while providing no intuitive understanding of model capabilities. This deficiency creates a dangerous disconnect between reported performance and practical abilities, particularly for applications requiring physical world understanding. We introduce LTD-Bench, a breakthrough benchmark that transforms LLM evaluation from abstract scores to directly observable visual outputs by requiring models to generate drawings through dot matrices or executable code. This approach makes spatial reasoning limitations immediately apparent even to non-experts, bridging the fundamental gap between statistical performance and intuitive assessment. LTD-Bench implements a comprehensive methodology with complementary generation tasks (testing spatial imagination) and recognition tasks (assessing spatial perception) across three progressively challenging difficulty levels, methodically evaluating both directions of the critical language-spatial mapping. Our extensive experiments with state-of-the-art models expose an alarming capability gap: even LLMs achieving impressive results on traditional benchmarks demonstrate profound deficiencies in establishing bidirectional mappings between language and spatial concept--a fundamental limitation that undermines their potential as genuine world models. Furthermore, LTD-Bench's visual outputs enable powerful diagnostic analysis, offering a potential approach to investigate model similarity.
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            <a href="https://www.alphaxiv.org/abs/2511.02288v1" target="_blank" rel="noopener noreferrer">
                用于手写数学表达式结构识别的链接预测图神经网络
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            Link prediction Graph Neural Networks for structure recognition of Handwritten Mathematical Expressions
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Cuong Tuan Nguyen, Ngoc Tuan Nguyen, Triet Hoang Minh Dao, Huy Minh Nhat, Huy Tr...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于手写数学表达式的结构识别，这是一个计算机视觉和文档分析的特定领域应用。虽然使用了图神经网络，但其应用场景与推荐系统、搜索或广告的核心领域没有直接关联，也不涉及任何可能在这些领域应用的LLM或Transformer技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 06:04:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02288v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02288v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We propose a Graph Neural Network (GNN)-based approach for Handwritten Mathematical Expression (HME) recognition by modeling HMEs as graphs, where nodes represent symbols and edges capture spatial dependencies. A deep BLSTM network is used for symbol segmentation, recognition, and spatial relation classification, forming an initial primitive graph. A 2D-CFG parser then generates all possible spatial relations, while the GNN-based link prediction model refines the structure by removing unnecessary connections, ultimately forming the Symbol Label Graph. Experimental results demonstrate the effectiveness of our approach, showing promising performance in HME structure recognition.
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            <a href="https://www.alphaxiv.org/abs/2511.02832v1" target="_blank" rel="noopener noreferrer">
                TWIST2：可扩展、可移植且全面的人形机器人数据收集系统
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            TWIST2: Scalable, Portable, and Holistic Humanoid Data Collection System
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yanjie Ze, Siheng Zhao, Weizhuo Wang, Angjoo Kanazawa, Rocky Duan, Pieter Abbeel...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于人形机器人数据收集系统，属于机器人学领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然数据收集系统在概念上可能涉及数据处理，但该工作的具体应用场景和技术方向与我的关注焦点完全无关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 18:58:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02832v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02832v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large-scale data has driven breakthroughs in robotics, from language models to vision-language-action models in bimanual manipulation. However, humanoid robotics lacks equally effective data collection frameworks. Existing humanoid teleoperation systems either use decoupled control or depend on expensive motion capture setups. We introduce TWIST2, a portable, mocap-free humanoid teleoperation and data collection system that preserves full whole-body control while advancing scalability. Our system leverages PICO4U VR for obtaining real-time whole-body human motions, with a custom 2-DoF robot neck (cost around $250) for egocentric vision, enabling holistic human-to-humanoid control. We demonstrate long-horizon dexterous and mobile humanoid skills and we can collect 100 demonstrations in 15 minutes with an almost 100% success rate. Building on this pipeline, we propose a hierarchical visuomotor policy framework that autonomously controls the full humanoid body based on egocentric vision. Our visuomotor policy successfully demonstrates whole-body dexterous manipulation and dynamic kicking tasks. The entire system is fully reproducible and open-sourced at https://yanjieze.com/TWIST2 . Our collected dataset is also open-sourced at https://twist-data.github.io .
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            <a href="https://www.alphaxiv.org/abs/2511.02830v1" target="_blank" rel="noopener noreferrer">
                Densemarks：通过点轨迹学习人头图像的标准嵌入
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Densemarks: Learning Canonical Embeddings for Human Heads Images via Point Tracks
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dmitrii Pozdeev, Alexey Artemov, Ananta R. Bhattarai, Artem Sevastopolsky
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉领域的人头图像处理和3D重建，与推荐系统、搜索或广告的核心技术没有直接关联。虽然涉及嵌入学习技术，但其应用场景（人头图像处理）和具体方法（点轨迹）与当前关注的LLM技术、推荐系统架构或Transformer改进等方向缺乏明确的连接点。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 18:58:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02830v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02830v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We propose DenseMarks - a new learned representation for human heads, enabling high-quality dense correspondences of human head images. For a 2D image of a human head, a Vision Transformer network predicts a 3D embedding for each pixel, which corresponds to a location in a 3D canonical unit cube. In order to train our network, we collect a dataset of pairwise point matches, estimated by a state-of-the-art point tracker over a collection of diverse in-the-wild talking heads videos, and guide the mapping via a contrastive loss, encouraging matched points to have close embeddings. We further employ multi-task learning with face landmarks and segmentation constraints, as well as imposing spatial continuity of embeddings through latent cube features, which results in an interpretable and queryable canonical space. The representation can be used for finding common semantic parts, face/head tracking, and stereo reconstruction. Due to the strong supervision, our method is robust to pose variations and covers the entire head, including hair. Additionally, the canonical space bottleneck makes sure the obtained representations are consistent across diverse poses and individuals. We demonstrate state-of-the-art results in geometry-aware point matching and monocular head tracking with 3D Morphable Models. The code and the model checkpoint will be made available to the public.
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            <a href="https://www.alphaxiv.org/abs/2511.02826v1" target="_blank" rel="noopener noreferrer">
                PLUTO-4：前沿病理学基础模型
            </a>
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            PLUTO-4: Frontier Pathology Foundation Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Harshith Padigela, Shima Nofallah, Atchuth Naveen Chilaparasetti, Ryun Han, Andr...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于病理学领域的医学应用，属于明确的医学领域特定应用，与推荐系统、搜索或广告技术完全无关。根据用户指定的无关主题列表，医学、生物学等特定领域应用应被排除在关注范围之外。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 18:54:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02826v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02826v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Foundation models trained on large-scale pathology image corpora have demonstrated strong transfer capabilities across diverse histopathology tasks. Building on this progress, we introduce PLUTO-4, our next generation of pathology foundation models that extend the Pathology-Universal Transformer (PLUTO) to frontier scale. We share two complementary Vision Transformer architectures in the PLUTO-4 family: a compact and efficient PLUTO-4S model optimized for multi-scale deployment using a FlexiViT setup with 2D-RoPE embeddings, and a frontier-scale PLUTO-4G model trained with a single patch size to maximize representation capacity and stability. Both models are pretrained using a self-supervised objective derived from DINOv2 on a large multi-institutional corpus containing 551,164 WSIs from 137,144 patients across over 50 institutions, spanning over 60 disease types and over 100 stains. Comprehensive evaluation across public and internal benchmarks demonstrates that PLUTO-4 achieves state-of-the-art performance on tasks requiring varying spatial and biological context, including patch-level classification, segmentation, and slide-level diagnosis. The compact PLUTO-4S provides high-throughput and robust performance for practical deployment, while PLUTO-4G establishes new performance frontiers across multiple pathology benchmarks, including an 11% improvement in dermatopathology diagnosis. These diverse improvements underscore PLUTO-4's potential to transform real-world applications as a backbone for translational research and diagnostic use cases.
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            <a href="https://www.alphaxiv.org/abs/2511.02791v1" target="_blank" rel="noopener noreferrer">
                AI生成图像检测：实证研究与未来研究方向
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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            AI-Generated Image Detection: An Empirical Study and Future Research Directions
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nusrat Tasnim, Kutub Uddin, Khalid Mahmood Malik
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于AI生成内容的检测技术，属于内容安全与验证领域，与推荐系统、搜索或广告的核心技术进展无关。虽然可能涉及多模态数据处理，但主要关注点在于检测而非推荐或搜索应用，且属于AIGC相关范畴，属于明确的无关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 18:13:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02791v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02791v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.GT</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The threats posed by AI-generated media, particularly deepfakes, are now raising significant challenges for multimedia forensics, misinformation detection, and biometric system resulting in erosion of public trust in the legal system, significant increase in frauds, and social engineering attacks. Although several forensic methods have been proposed, they suffer from three critical gaps: (i) use of non-standardized benchmarks with GAN- or diffusion-generated images, (ii) inconsistent training protocols (e.g., scratch, frozen, fine-tuning), and (iii) limited evaluation metrics that fail to capture generalization and explainability. These limitations hinder fair comparison, obscure true robustness, and restrict deployment in security-critical applications. This paper introduces a unified benchmarking framework for systematic evaluation of forensic methods under controlled and reproducible conditions. We benchmark ten SoTA forensic methods (scratch, frozen, and fine-tuned) and seven publicly available datasets (GAN and diffusion) to perform extensive and systematic evaluations. We evaluate performance using multiple metrics, including accuracy, average precision, ROC-AUC, error rate, and class-wise sensitivity. We also further analyze model interpretability using confidence curves and Grad-CAM heatmaps. Our evaluations demonstrate substantial variability in generalization, with certain methods exhibiting strong in-distribution performance but degraded cross-model transferability. This study aims to guide the research community toward a deeper understanding of the strengths and limitations of current forensic approaches, and to inspire the development of more robust, generalizable, and explainable solutions.
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            <a href="https://www.alphaxiv.org/abs/2511.02779v1" target="_blank" rel="noopener noreferrer">
                当可视化成为推理的第一步：MIRA，一个视觉思维链基准
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            When Visualizing is the First Step to Reasoning: MIRA, a Benchmark for Visual Chain-of-Thought
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yiyang Zhou, Haoqin Tu, Zijun Wang, Zeyu Wang, Niklas Muennighoff, Fan Nie, Yeji...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视觉推理基准和思维链评估，属于纯粹的视觉和NLP评估领域。虽然提到了思维链概念，但主要应用于视觉推理任务，没有明确的推荐系统、搜索或广告应用潜力。该工作更侧重于基准创建和评估方法，而非能够应用于推荐、搜索或广告领域的技术进步。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 18:00:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02779v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02779v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We propose MIRA, a new benchmark designed to evaluate models in scenarios where generating intermediate visual images is essential for successful reasoning. Unlike traditional CoT methods that rely solely on text, tasks in MIRA require models to generate and utilize intermediate images - such as sketches, structural diagrams, or path drawings - to guide their reasoning process. This setup closely mirrors how humans solve complex problems through "drawing to think". To solve this, MIRA focuses on tasks that are intrinsically challenging and involve complex structures, spatial relationships, or reasoning steps that are difficult to express through language alone. To ensure that our evaluation data is of high-quality, we include 546 multimodal problems, annotated with intermediate visual images and final answers. We also propose a unified evaluation protocol for MIRA that spans three levels of evaluation input: direct input with image and question only, text-only CoT input with image and thinking prompts, and Visual-CoT input with both annotated image clues and textual thinking prompts. To probe the upper bound of model capacity on our benchmark, we also report pass@k and majority voting accuracies under different k settings. Experimental results show that existing multimodal large language models, including strongest private models as well as strong open-weight models, perform poorly when relying solely on textual prompts. However, when intermediate visual cues are provided, model performance improves consistently, yielding an average relative gain of 33.7% across all models and tasks. We also probe the upper bound by expanding the search space and designing textual prompts aligned with Visual-CoT, but both yield only limited improvements compared to our Visual-CoT setting. These results underscore the critical role of imagined visual information in enabling successful reasoning on MIRA.
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            <a href="https://www.alphaxiv.org/abs/2511.02777v1" target="_blank" rel="noopener noreferrer">
                PercHead：用于单图像3D头部重建与编辑的感知头部模型
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            PercHead: Perceptual Head Model for Single-Image 3D Head Reconstruction & Editing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Antonio Oroz, Matthias Nießner, Tobias Kirschstein
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D头部重建和编辑的计算机视觉任务，属于纯粹的视觉领域研究。虽然标题提到'感知'，但这指的是视觉感知而非推荐系统相关的用户感知。该工作没有展示与搜索、推荐或广告系统的任何潜在应用连接，完全落在不相关主题范围内。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 17:59:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02777v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02777v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    We present PercHead, a method for single-image 3D head reconstruction and semantic 3D editing - two tasks that are inherently challenging due to severe view occlusions, weak perceptual supervision, and the ambiguity of editing in 3D space. We develop a unified base model for reconstructing view-consistent 3D heads from a single input image. The model employs a dual-branch encoder followed by a ViT-based decoder that lifts 2D features into 3D space through iterative cross-attention. Rendering is performed using Gaussian Splatting. At the heart of our approach is a novel perceptual supervision strategy based on DINOv2 and SAM2.1, which provides rich, generalized signals for both geometric and appearance fidelity. Our model achieves state-of-the-art performance in novel-view synthesis and, furthermore, exhibits exceptional robustness to extreme viewing angles compared to established baselines. Furthermore, this base model can be seamlessly extended for semantic 3D editing by swapping the encoder and finetuning the network. In this variant, we disentangle geometry and style through two distinct input modalities: a segmentation map to control geometry and either a text prompt or a reference image to specify appearance. We highlight the intuitive and powerful 3D editing capabilities of our model through a lightweight, interactive GUI, where users can effortlessly sculpt geometry by drawing segmentation maps and stylize appearance via natural language or image prompts. Project Page: https://antoniooroz.github.io/PercHead Video: https://www.youtube.com/watch?v=4hFybgTk4kE
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            <a href="https://www.alphaxiv.org/abs/2511.02720v1" target="_blank" rel="noopener noreferrer">
                LLEXICORP：卷积神经网络面向终端用户的解释性
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            LLEXICORP: End-user Explainability of Convolutional Neural Networks
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Vojtěch Kůr, Adam Bajger, Adam Kukučka, Marek Hradil, Vít Musil, Tomáš Brázdil
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于卷积神经网络（CNN）的解释性，这是计算机视觉领域的特定技术，与推荐系统、搜索或广告的核心进展没有直接关联。CNN解释性主要服务于视觉模型理解，在当前聚焦的LLM技术、Transformer架构或异构数据统一建模等方向上缺乏明确的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 16:44:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02720v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02720v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Convolutional neural networks (CNNs) underpin many modern computer vision systems. With applications ranging from common to critical areas, a need to explain and understand the model and its decisions (XAI) emerged. Prior works suggest that in the top layers of CNNs, the individual channels can be attributed to classifying human-understandable concepts. Concept relevance propagation (CRP) methods can backtrack predictions to these channels and find images that most activate these channels. However, current CRP workflows are largely manual: experts must inspect activation images to name the discovered concepts and must synthesize verbose explanations from relevance maps, limiting the accessibility of the explanations and their scalability. To address these issues, we introduce Large Language model EXplaIns COncept Relevance Propagation (LLEXICORP), a modular pipeline that couples CRP with a multimodal large language model. Our approach automatically assigns descriptive names to concept prototypes and generates natural-language explanations that translate quantitative relevance distributions into intuitive narratives. To ensure faithfulness, we craft prompts that teach the language model the semantics of CRP through examples and enforce a separation between naming and explanation tasks. The resulting text can be tailored to different audiences, offering low-level technical descriptions for experts and high-level summaries for non-technical stakeholders. We qualitatively evaluate our method on various images from ImageNet on a VGG16 model. Our findings suggest that integrating concept-based attribution methods with large language models can significantly lower the barrier to interpreting deep neural networks, paving the way for more transparent AI systems.
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            <a href="https://www.alphaxiv.org/abs/2511.02717v1" target="_blank" rel="noopener noreferrer">
                一种用于实时输入-参数-状态估计的无迹卡尔曼滤波方法
            </a>
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            An unscented Kalman filter method for real time input-parameter-state estimation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Marios Impraimakis, Andrew W. Smyth
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于卡尔曼滤波方法，属于传统的信号处理和状态估计领域，与推荐系统、搜索或广告的核心技术栈没有直接关联。无迹卡尔曼滤波主要应用于控制系统、导航和物理系统建模，在当前LLM和Transformer主导的推荐/搜索技术趋势下缺乏明确的适用性。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 16:39:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02717v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02717v1
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                <div class="flex flex-wrap"><span class="category-tag">eess.SP</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.SY</span><span class="category-tag">eess.AS</span><span class="category-tag">eess.SY</span><span class="category-tag">68T05 (Learning and adaptive systems)</span><span class="category-tag">I.2.6; I.2.8</span></div>
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                    The input-parameter-state estimation capabilities of a novel unscented Kalman filter is examined herein on both linear and nonlinear systems. The unknown input is estimated in two stages within each time step. Firstly, the predicted dynamic states and the system parameters provide an estimation of the input. Secondly, the corrected with measurements states and parameters provide a final estimation. Importantly, it is demonstrated using the perturbation analysis that, a system with at least a zero or a non-zero known input can potentially be uniquely identified. This output-only methodology allows for a better understanding of the system compared to classical output-only parameter identification strategies, given that all the dynamic states, the parameters, and the input are estimated jointly and in real-time.
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            <a href="https://www.alphaxiv.org/abs/2511.02712v1" target="_blank" rel="noopener noreferrer">
                VidEmo：面向以情感为中心的视频基础模型的情感树推理
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            VidEmo: Affective-Tree Reasoning for Emotion-Centric Video Foundation Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhicheng Zhang, Weicheng Wang, Yongjie Zhu, Wenyu Qin, Pengfei Wan, Di Zhang, Ju...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频情感分析这一特定领域，与推荐系统、搜索或广告的核心技术进展无关。情感树推理属于情感计算范畴，没有明确的路径可以应用于推荐、搜索或广告的排序、匹配或架构改进。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 16:31:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02712v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02712v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Understanding and predicting emotion from videos has gathered significant attention in recent studies, driven by advancements in video large language models (VideoLLMs). While advanced methods have made progress in video emotion analysis, the intrinsic nature of emotions poses significant challenges. Emotions are characterized by dynamic and cues-dependent properties, making it difficult to understand complex and evolving emotional states with reasonable rationale. To tackle these challenges, we propose a novel affective cues-guided reasoning framework that unifies fundamental attribute perception, expression analysis, and high-level emotional understanding in a stage-wise manner. At the core of our approach is a family of video emotion foundation models (VidEmo), specifically designed for emotion reasoning and instruction-following. These models undergo a two-stage tuning process: first, curriculum emotion learning for injecting emotion knowledge, followed by affective-tree reinforcement learning for emotion reasoning. Moreover, we establish a foundational data infrastructure and introduce a emotion-centric fine-grained dataset (Emo-CFG) consisting of 2.1M diverse instruction-based samples. Emo-CFG includes explainable emotional question-answering, fine-grained captions, and associated rationales, providing essential resources for advancing emotion understanding tasks. Experimental results demonstrate that our approach achieves competitive performance, setting a new milestone across 15 face perception tasks.
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            <a href="https://www.alphaxiv.org/abs/2511.02645v1" target="_blank" rel="noopener noreferrer">
                基于单张图像的鲁棒人脸活体检测用于生物特征认证
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            Robust Face Liveness Detection for Biometric Authentication using Single Image
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Poulami Raha, Yeongnam Chae
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的人脸活体检测和生物特征认证，属于纯粹的视觉技术应用。虽然人脸识别在广告定向中可能有间接应用，但该论文的核心技术（活体检测）与推荐系统、搜索或广告排名没有直接关联，也不涉及LLM、Transformer架构或异构数据建模等当前关注的技术方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 15:13:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02645v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02645v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Biometric technologies are widely adopted in security, legal, and financial systems. Face recognition can authenticate a person based on the unique facial features such as shape and texture. However, recent works have demonstrated the vulnerability of Face Recognition Systems (FRS) towards presentation attacks. Using spoofing (aka.,presentation attacks), a malicious actor can get illegitimate access to secure systems. This paper proposes a novel light-weight CNN framework to identify print/display, video and wrap attacks. The proposed robust architecture provides seamless liveness detection ensuring faster biometric authentication (1-2 seconds on CPU). Further, this also presents a newly created 2D spoof attack dataset consisting of more than 500 videos collected from 60 subjects. To validate the effectiveness of this architecture, we provide a demonstration video depicting print/display, video and wrap attack detection approaches. The demo can be viewed in the following link: https://rak.box.com/s/m1uf31fn5amtjp4mkgf1huh4ykfeibaa
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            <a href="https://www.alphaxiv.org/abs/2511.02591v1" target="_blank" rel="noopener noreferrer">
                野外环境下的零样本多动物追踪
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        <div class="mb-2 text-base text-gray-700">
            Zero-Shot Multi-Animal Tracking in the Wild
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jan Frederik Meier, Timo Lüddecke
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的多目标跟踪任务，应用于动物行为分析领域。这与推荐系统、搜索或广告的核心技术领域没有直接关联，也不涉及LLM、Transformer架构或异构数据建模等关键技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 14:12:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02591v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02591v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multi-animal tracking is crucial for understanding animal ecology and behavior. However, it remains a challenging task due to variations in habitat, motion patterns, and species appearance. Traditional approaches typically require extensive model fine-tuning and heuristic design for each application scenario. In this work, we explore the potential of recent vision foundation models for zero-shot multi-animal tracking. By combining a Grounding Dino object detector with the Segment Anything Model 2 (SAM 2) tracker and carefully designed heuristics, we develop a tracking framework that can be applied to new datasets without any retraining or hyperparameter adaptation. Evaluations on ChimpAct, Bird Flock Tracking, AnimalTrack, and a subset of GMOT-40 demonstrate strong and consistent performance across diverse species and environments. The code is available at https://github.com/ecker-lab/SAM2-Animal-Tracking.
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            <a href="https://www.alphaxiv.org/abs/2511.02580v1" target="_blank" rel="noopener noreferrer">
                TAUE：免训练噪声移植与培育扩散模型
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            TAUE: Training-free Noise Transplant and Cultivation Diffusion Model
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Daichi Nagai, Ryugo Morita, Shunsuke Kitada, Hitoshi Iyatomi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于扩散模型的训练方法改进，属于生成式AI领域。虽然扩散模型在AIGC和内容生成中应用广泛，但论文并未显示与推荐系统、搜索或广告排名的直接关联，也不涉及Transformer架构改进或异构数据统一建模等核心技术方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 13:56:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02580v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02580v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.GR</span><span class="category-tag">cs.LG</span></div>
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                    Despite the remarkable success of text-to-image diffusion models, their output of a single, flattened image remains a critical bottleneck for professional applications requiring layer-wise control. Existing solutions either rely on fine-tuning with large, inaccessible datasets or are training-free yet limited to generating isolated foreground elements, failing to produce a complete and coherent scene. To address this, we introduce the Training-free Noise Transplantation and Cultivation Diffusion Model (TAUE), a novel framework for zero-shot, layer-wise image generation. Our core technique, Noise Transplantation and Cultivation (NTC), extracts intermediate latent representations from both foreground and composite generation processes, transplanting them into the initial noise for subsequent layers. This ensures semantic and structural coherence across foreground, background, and composite layers, enabling consistent, multi-layered outputs without requiring fine-tuning or auxiliary datasets. Extensive experiments show that our training-free method achieves performance comparable to fine-tuned methods, enhancing layer-wise consistency while maintaining high image quality and fidelity. TAUE not only eliminates costly training and dataset requirements but also unlocks novel downstream applications, such as complex compositional editing, paving the way for more accessible and controllable generative workflows.
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            <a href="https://www.alphaxiv.org/abs/2511.02565v1" target="_blank" rel="noopener noreferrer">
                一种受认知过程启发的、与主体无关的脑视觉解码架构
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            A Cognitive Process-Inspired Architecture for Subject-Agnostic Brain Visual Decoding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jingyu Lu, Haonan Wang, Qixiang Zhang, Xiaomeng Li
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于脑视觉解码和认知过程建模，属于神经科学和生物医学领域，与推荐系统、搜索或广告的核心技术完全无关。论文内容涉及脑机接口和神经信号处理，没有任何明显的应用场景可以转化到推荐、搜索或广告领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 13:39:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02565v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02565v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Subject-agnostic brain decoding, which aims to reconstruct continuous visual experiences from fMRI without subject-specific training, holds great potential for clinical applications. However, this direction remains underexplored due to challenges in cross-subject generalization and the complex nature of brain signals. In this work, we propose Visual Cortex Flow Architecture (VCFlow), a novel hierarchical decoding framework that explicitly models the ventral-dorsal architecture of the human visual system to learn multi-dimensional representations. By disentangling and leveraging features from early visual cortex, ventral, and dorsal streams, VCFlow captures diverse and complementary cognitive information essential for visual reconstruction. Furthermore, we introduce a feature-level contrastive learning strategy to enhance the extraction of subject-invariant semantic representations, thereby enhancing subject-agnostic applicability to previously unseen subjects. Unlike conventional pipelines that need more than 12 hours of per-subject data and heavy computation, VCFlow sacrifices only 7\% accuracy on average yet generates each reconstructed video in 10 seconds without any retraining, offering a fast and clinically scalable solution. The source code will be released upon acceptance of the paper.
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            <a href="https://www.alphaxiv.org/abs/2511.02560v1" target="_blank" rel="noopener noreferrer">
                SigmaCollab：一个面向物理协同场景应用的数据集
            </a>
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        <div class="mb-2 text-base text-gray-700">
            SigmaCollab: An Application-Driven Dataset for Physically Situated Collaboration
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dan Bohus, Sean Andrist, Ann Paradiso, Nick Saw, Tim Schoonbeek, Maia Stiber
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于物理协同场景的专用数据集构建，属于特定应用领域的数据集工作。这与我关注的推荐系统、搜索广告、LLM技术及Transformer架构等核心方向完全无关，也不涉及多模态建模在异构数据上的类比应用。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 13:30:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02560v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02560v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.HC</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We introduce SigmaCollab, a dataset enabling research on physically situated human-AI collaboration. The dataset consists of a set of 85 sessions in which untrained participants were guided by a mixed-reality assistive AI agent in performing procedural tasks in the physical world. SigmaCollab includes a set of rich, multimodal data streams, such as the participant and system audio, egocentric camera views from the head-mounted device, depth maps, head, hand and gaze tracking information, as well as additional annotations performed post-hoc. While the dataset is relatively small in size (~ 14 hours), its application-driven and interactive nature brings to the fore novel research challenges for human-AI collaboration, and provides more realistic testing grounds for various AI models operating in this space. In future work, we plan to use the dataset to construct a set of benchmarks for physically situated collaboration in mixed-reality task assistive scenarios. SigmaCollab is available at https://github.com/microsoft/SigmaCollab.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02558v1" target="_blank" rel="noopener noreferrer">
                预测未来解剖结构：纵向脑部MRI到MRI预测
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Forecasting Future Anatomies: Longitudianl Brain Mri-to-Mri Prediction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ali Farki, Elaheh Moradi, Deepika Koundal, Jussi Tohka
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像领域的脑部MRI预测，属于医学/生物学特定领域应用。这与我的关注点完全无关，我的关注点是推荐系统、搜索、广告及相关LLM/Transformer技术，不涉及医疗、生物或其他领域特定应用。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 13:19:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02558v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02558v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span><span class="category-tag">q-bio.NC</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Predicting future brain state from a baseline magnetic resonance image (MRI) is a central challenge in neuroimaging and has important implications for studying neurodegenerative diseases such as Alzheimer's disease (AD). Most existing approaches predict future cognitive scores or clinical outcomes, such as conversion from mild cognitive impairment to dementia. Instead, here we investigate longitudinal MRI image-to-image prediction that forecasts a participant's entire brain MRI several years into the future, intrinsically modeling complex, spatially distributed neurodegenerative patterns. We implement and evaluate five deep learning architectures (UNet, U2-Net, UNETR, Time-Embedding UNet, and ODE-UNet) on two longitudinal cohorts (ADNI and AIBL). Predicted follow-up MRIs are directly compared with the actual follow-up scans using metrics that capture global similarity and local differences. The best performing models achieve high-fidelity predictions, and all models generalize well to an independent external dataset, demonstrating robust cross-cohort performance. Our results indicate that deep learning can reliably predict participant-specific brain MRI at the voxel level, offering new opportunities for individualized prognosis.
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            <a href="https://www.alphaxiv.org/abs/2511.02541v1" target="_blank" rel="noopener noreferrer">
                工业缺陷检测的无监督学习：基于剪切散斑数据的案例研究
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        <div class="mb-2 text-base text-gray-700">
            Unsupervised Learning for Industrial Defect Detection: A Case Study on Shearographic Data
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jessica Plassmann, Nicolas Schuler, Georg von Freymann, Michael Schuth
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于工业缺陷检测和剪切散斑数据，属于计算机视觉在工业检测领域的特定应用，与推荐系统、搜索或广告的核心技术领域完全无关。论文内容涉及工业质量控制而非用户行为建模、内容排序或广告投放等核心关注领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 12:48:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02541v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02541v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Shearography is a non-destructive testing method for detecting subsurface defects, offering high sensitivity and full-field inspection capabilities. However, its industrial adoption remains limited due to the need for expert interpretation. To reduce reliance on labeled data and manual evaluation, this study explores unsupervised learning methods for automated anomaly detection in shearographic images. Three architectures are evaluated: a fully connected autoencoder, a convolutional autoencoder, and a student-teacher feature matching model. All models are trained solely on defect-free data. A controlled dataset was developed using a custom specimen with reproducible defect patterns, enabling systematic acquisition of shearographic measurements under both ideal and realistic deformation conditions. Two training subsets were defined: one containing only undistorted, defect-free samples, and one additionally including globally deformed, yet defect-free, data. The latter simulates practical inspection conditions by incorporating deformation-induced fringe patterns that may obscure localized anomalies. The models are evaluated in terms of binary classification and, for the student-teacher model, spatial defect localization. Results show that the student-teacher approach achieves superior classification robustness and enables precise localization. Compared to the autoencoder-based models, it demonstrates improved separability of feature representations, as visualized through t-SNE embeddings. Additionally, a YOLOv8 model trained on labeled defect data serves as a reference to benchmark localization quality. This study underscores the potential of unsupervised deep learning for scalable, label-efficient shearographic inspection in industrial environments.
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            <a href="https://www.alphaxiv.org/abs/2511.02507v1" target="_blank" rel="noopener noreferrer">
                保持本地化、轻量化与真实性：面向机电一体化认知系统的边缘计算设备自动报告生成
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Keeping it Local, Tiny and Real: Automated Report Generation on Edge Computing Devices for Mechatronic-Based Cognitive Systems
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nicolas Schuler, Lea Dewald, Jürgen Graf
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于边缘计算设备上的自动报告生成，应用于机电一体化认知系统，这属于特定领域应用（机电一体化）和边缘计算技术。论文标题未提及推荐系统、搜索、广告、LLM技术或Transformer架构的任何方面，也没有展示在异构数据统一建模方面的潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 11:49:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02507v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02507v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advancements in Deep Learning enable hardware-based cognitive systems, that is, mechatronic systems in general and robotics in particular with integrated Artificial Intelligence, to interact with dynamic and unstructured environments. While the results are impressive, the application of such systems to critical tasks like autonomous driving as well as service and care robotics necessitate the evaluation of large amount of heterogeneous data. Automated report generation for Mobile Robotics can play a crucial role in facilitating the evaluation and acceptance of such systems in various domains. In this paper, we propose a pipeline for generating automated reports in natural language utilizing various multi-modal sensors that solely relies on local models capable of being deployed on edge computing devices, thus preserving the privacy of all actors involved and eliminating the need for external services. In particular, we evaluate our implementation on a diverse dataset spanning multiple domains including indoor, outdoor and urban environments, providing quantitative as well as qualitative evaluation results. Various generated example reports and other supplementary materials are available via a public repository.
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            <a href="https://www.alphaxiv.org/abs/2511.02505v1" target="_blank" rel="noopener noreferrer">
                ESA：基于能量的镜头组装优化用于自动视频编辑
            </a>
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        <div class="mb-2 text-base text-gray-700">
            ESA: Energy-Based Shot Assembly Optimization for Automatic Video Editing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yaosen Chen, Wei Wang, Xuming Wen, Han Yang, Yanru Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于自动视频编辑中的镜头组装优化，这属于内容生成领域，与我的核心关注点（推荐系统、搜索、广告中的排序和建模）无关。虽然提到了能量基模型，但应用于视频编辑场景，没有明确的推荐/搜索/广告应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 11:48:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02505v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02505v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Shot assembly is a crucial step in film production and video editing, involving the sequencing and arrangement of shots to construct a narrative, convey information, or evoke emotions. Traditionally, this process has been manually executed by experienced editors. While current intelligent video editing technologies can handle some automated video editing tasks, they often fail to capture the creator's unique artistic expression in shot assembly.To address this challenge, we propose an energy-based optimization method for video shot assembly. Specifically, we first perform visual-semantic matching between the script generated by a large language model and a video library to obtain subsets of candidate shots aligned with the script semantics. Next, we segment and label the shots from reference videos, extracting attributes such as shot size, camera motion, and semantics. We then employ energy-based models to learn from these attributes, scoring candidate shot sequences based on their alignment with reference styles. Finally, we achieve shot assembly optimization by combining multiple syntax rules, producing videos that align with the assembly style of the reference videos. Our method not only automates the arrangement and combination of independent shots according to specific logic, narrative requirements, or artistic styles but also learns the assembly style of reference videos, creating a coherent visual sequence or holistic visual expression. With our system, even users with no prior video editing experience can create visually compelling videos. Project page: https://sobeymil.github.io/esa.com
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            <a href="https://www.alphaxiv.org/abs/2511.02503v1" target="_blank" rel="noopener noreferrer">
                在低数据场景下为X射线叠层衍射成像适配通用基础模型
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        <div class="mb-2 text-base text-gray-700">
            Adapting General-Purpose Foundation Models for X-ray Ptychography in Low-Data Regimes
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Robinson Umeike, Neil Getty, Yin Xiangyu, Yi Jiang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于X射线叠层衍射成像这一特定科学成像领域，属于物理/医学成像应用范畴。虽然涉及基础模型适配，但其应用领域完全属于被排除的医学/物理领域，与推荐系统、搜索或广告没有任何潜在关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 11:43:05
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02503v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02503v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The automation of workflows in advanced microscopy is a key goal where foundation models like Language Models (LLMs) and Vision-Language Models (VLMs) show great potential. However, adapting these general-purpose models for specialized scientific tasks is critical, and the optimal domain adaptation strategy is often unclear. To address this, we introduce PtychoBench, a new multi-modal, multi-task benchmark for ptychographic analysis. Using this benchmark, we systematically compare two specialization strategies: Supervised Fine-Tuning (SFT) and In-Context Learning (ICL). We evaluate these strategies on a visual artifact detection task with VLMs and a textual parameter recommendation task with LLMs in a data-scarce regime. Our findings reveal that the optimal specialization pathway is task-dependent. For the visual task, SFT and ICL are highly complementary, with a fine-tuned model guided by context-aware examples achieving the highest mean performance (Micro-F1 of 0.728). Conversely, for the textual task, ICL on a large base model is the superior strategy, reaching a peak Micro-F1 of 0.847 and outperforming a powerful "super-expert" SFT model (0-shot Micro-F1 of 0.839). We also confirm the superiority of context-aware prompting and identify a consistent contextual interference phenomenon in fine-tuned models. These results, benchmarked against strong baselines including GPT-4o and a DINOv3-based classifier, offer key observations for AI in science: the optimal specialization path in our benchmark is dependent on the task modality, offering a clear framework for developing more effective science-based agentic systems.
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            <a href="https://www.alphaxiv.org/abs/2511.02489v1" target="_blank" rel="noopener noreferrer">
                目标检测作为可选基础：一种用于跨视角无人机定位的图匹配网络
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            Object Detection as an Optional Basis: A Graph Matching Network for Cross-View UAV Localization
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tao Liu, Kan Ren, Qian Chen
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的无人机定位和对象检测任务，属于纯粹的视觉应用领域。虽然提到了图匹配网络，但其应用场景（无人机跨视角定位）与推荐系统、搜索或广告领域没有直接关联，也不涉及LLM技术或Transformer架构的改进。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 11:25:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02489v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02489v1
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                    With the rapid growth of the low-altitude economy, UAVs have become crucial for measurement and tracking in patrol systems. However, in GNSS-denied areas, satellite-based localization methods are prone to failure. This paper presents a cross-view UAV localization framework that performs map matching via object detection, aimed at effectively addressing cross-temporal, cross-view, heterogeneous aerial image matching. In typical pipelines, UAV visual localization is formulated as an image-retrieval problem: features are extracted to build a localization map, and the pose of a query image is estimated by matching it to a reference database with known poses. Because publicly available UAV localization datasets are limited, many approaches recast localization as a classification task and rely on scene labels in these datasets to ensure accuracy. Other methods seek to reduce cross-domain differences using polar-coordinate reprojection, perspective transformations, or generative adversarial networks; however, they can suffer from misalignment, content loss, and limited realism. In contrast, we leverage modern object detection to accurately extract salient instances from UAV and satellite images, and integrate a graph neural network to reason about inter-image and intra-image node relationships. Using a fine-grained, graph-based node-similarity metric, our method achieves strong retrieval and localization performance. Extensive experiments on public and real-world datasets show that our approach handles heterogeneous appearance differences effectively and generalizes well, making it applicable to scenarios with larger modality gaps, such as infrared-visible image matching. Our dataset will be publicly available at the following URL: https://github.com/liutao23/ODGNNLoc.git.
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            <a href="https://www.alphaxiv.org/abs/2511.02468v1" target="_blank" rel="noopener noreferrer">
                HAGI++：头部辅助的视线插补与生成
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            HAGI++: Head-Assisted Gaze Imputation and Generation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chuhan Jiao, Zhiming Hu, Andreas Bulling
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明研究焦点在视线追踪和生成技术上，这属于计算机视觉领域，与推荐系统、搜索或广告的核心技术栈没有直接关联。视线追踪技术虽然可能在某些人机交互场景中有应用，但论文标题并未显示出与异构数据建模、Transformer架构或LLM技术的任何联系，因此对当前关注领域基本不相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 10:51:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02468v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02468v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.HC</span><span class="category-tag">cs.CV</span></div>
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                    Mobile eye tracking plays a vital role in capturing human visual attention across both real-world and extended reality (XR) environments, making it an essential tool for applications ranging from behavioural research to human-computer interaction. However, missing values due to blinks, pupil detection errors, or illumination changes pose significant challenges for further gaze data analysis. To address this challenge, we introduce HAGI++ - a multi-modal diffusion-based approach for gaze data imputation that, for the first time, uses the integrated head orientation sensors to exploit the inherent correlation between head and eye movements. HAGI++ employs a transformer-based diffusion model to learn cross-modal dependencies between eye and head representations and can be readily extended to incorporate additional body movements. Extensive evaluations on the large-scale Nymeria, Ego-Exo4D, and HOT3D datasets demonstrate that HAGI++ consistently outperforms conventional interpolation methods and deep learning-based time-series imputation baselines in gaze imputation. Furthermore, statistical analyses confirm that HAGI++ produces gaze velocity distributions that closely match actual human gaze behaviour, ensuring more realistic gaze imputations. Moreover, by incorporating wrist motion captured from commercial wearable devices, HAGI++ surpasses prior methods that rely on full-body motion capture in the extreme case of 100% missing gaze data (pure gaze generation). Our method paves the way for more complete and accurate eye gaze recordings in real-world settings and has significant potential for enhancing gaze-based analysis and interaction across various application domains.
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            <a href="https://www.alphaxiv.org/abs/2511.02417v1" target="_blank" rel="noopener noreferrer">
                合成作物-杂草图像生成及其对模型泛化能力的影响
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            Synthetic Crop-Weed Image Generation and its Impact on Model Generalization
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Garen Boyadjian, Cyrille Pierre, Johann Laconte, Riccardo Bertoglio
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于农业领域的计算机视觉应用，涉及作物和杂草的图像生成与模型泛化。虽然提到了模型泛化，但其核心应用场景（农业视觉识别）与推荐系统、搜索或广告领域没有直接关联。该研究属于纯粹的视觉应用范畴，不符合当前关注的任何技术方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 09:47:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02417v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02417v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                    Precise semantic segmentation of crops and weeds is necessary for agricultural weeding robots. However, training deep learning models requires large annotated datasets, which are costly to obtain in real fields. Synthetic data can reduce this burden, but the gap between simulated and real images remains a challenge. In this paper, we present a pipeline for procedural generation of synthetic crop-weed images using Blender, producing annotated datasets under diverse conditions of plant growth, weed density, lighting, and camera angle. We benchmark several state-of-the-art segmentation models on synthetic and real datasets and analyze their cross-domain generalization. Our results show that training on synthetic images leads to a sim-to-real gap of 10%, surpassing previous state-of-the-art methods. Moreover, synthetic data demonstrates good generalization properties, outperforming real datasets in cross-domain scenarios. These findings highlight the potential of synthetic agricultural datasets and support hybrid strategies for more efficient model training.
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            <a href="https://www.alphaxiv.org/abs/2511.02411v1" target="_blank" rel="noopener noreferrer">
                IllumFlow：基于条件整流流和Retinex分解的照明自适应低光增强
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            IllumFlow: Illumination-Adaptive Low-Light Enhancement via Conditional Rectified Flow and Retinex Decomposition
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenyang Wei, Yang yang, Xixi Jia, Xiangchu Feng, Weiwei Wang, Renzhen Wang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的低光图像增强技术，涉及整流流和Retinex分解方法。这与搜索、推荐或广告系统的核心领域进展、LLM技术或Transformer架构没有直接关联，也不涉及异构数据的统一建模。该技术主要面向图像处理应用，没有明显的推荐系统、搜索或广告应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 09:41:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02411v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02411v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    We present IllumFlow, a novel framework that synergizes conditional Rectified Flow (CRF) with Retinex theory for low-light image enhancement (LLIE). Our model addresses low-light enhancement through separate optimization of illumination and reflectance components, effectively handling both lighting variations and noise. Specifically, we first decompose an input image into reflectance and illumination components following Retinex theory. To model the wide dynamic range of illumination variations in low-light images, we propose a conditional rectified flow framework that represents illumination changes as a continuous flow field. While complex noise primarily resides in the reflectance component, we introduce a denoising network, enhanced by flow-derived data augmentation, to remove reflectance noise and chromatic aberration while preserving color fidelity. IllumFlow enables precise illumination adaptation across lighting conditions while naturally supporting customizable brightness enhancement. Extensive experiments on low-light enhancement and exposure correction demonstrate superior quantitative and qualitative performance over existing methods.
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                扰动但稳定：跨CNN、ViT和自监督ViT的人猫不变性表示
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            Purrturbed but Stable: Human-Cat Invariant Representations Across CNNs, ViTs and Self-Supervised ViTs
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Arya Shah, Vaibhav Tripathi
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的表示学习，特别是针对人猫图像的跨架构不变性研究。虽然涉及ViT架构，但论文内容纯粹是视觉领域的表示学习，没有明确的技术路径或潜在应用可以迁移到推荐系统、搜索或广告领域。论文的核心关注点与异构数据建模、Transformer效率提升或LLM在推荐/搜索中的应用无关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 09:35:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02404v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02404v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Cats and humans differ in ocular anatomy. Most notably, Felis Catus (domestic cats) have vertically elongated pupils linked to ambush predation; yet, how such specializations manifest in downstream visual representations remains incompletely understood. We present a unified, frozen-encoder benchmark that quantifies feline-human cross-species representational alignment in the wild, across convolutional networks, supervised Vision Transformers, windowed transformers, and self-supervised ViTs (DINO), using layer-wise Centered Kernel Alignment (linear and RBF) and Representational Similarity Analysis, with additional distributional and stability tests reported in the paper. Across models, DINO ViT-B/16 attains the most substantial alignment (mean CKA-RBF $\approx0.814$, mean CKA-linear $\approx0.745$, mean RSA $\approx0.698$), peaking at early blocks, indicating that token-level self-supervision induces early-stage features that bridge species-specific statistics. Supervised ViTs are competitive on CKA yet show weaker geometric correspondence than DINO (e.g., ViT-B/16 RSA $\approx0.53$ at block8; ViT-L/16 $\approx0.47$ at block14), revealing depth-dependent divergences between similarity and representational geometry. CNNs remain strong baselines but below plain ViTs on alignment, and windowed transformers underperform plain ViTs, implicating architectural inductive biases in cross-species alignment. Results indicate that self-supervision coupled with ViT inductive biases yields representational geometries that more closely align feline and human visual systems than widely used CNNs and windowed Transformers, providing testable neuroscientific hypotheses about where and how cross-species visual computations converge. We release our code and dataset for reference and reproducibility.
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            <a href="https://www.alphaxiv.org/abs/2511.02400v1" target="_blank" rel="noopener noreferrer">
                MammoClean：通过数据集协调实现乳腺X线摄影中可复现且偏见感知的人工智能
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            MammoClean: Toward Reproducible and Bias-Aware AI in Mammography through Dataset Harmonization
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yalda Zafari, Hongyi Pan, Gorkem Durak, Ulas Bagci, Essam A. Rashed, Mohamed Mab...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像（乳腺X线摄影）领域的数据集协调和偏见消除，属于医疗领域的特定应用。论文内容涉及数据偏见和可复现性，这些主题被明确列为无关主题，与推荐系统、搜索或广告的核心技术进展没有直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 09:29:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02400v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02400v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">eess.IV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    The development of clinically reliable artificial intelligence (AI) systems for mammography is hindered by profound heterogeneity in data quality, metadata standards, and population distributions across public datasets. This heterogeneity introduces dataset-specific biases that severely compromise the generalizability of the model, a fundamental barrier to clinical deployment. We present MammoClean, a public framework for standardization and bias quantification in mammography datasets. MammoClean standardizes case selection, image processing (including laterality and intensity correction), and unifies metadata into a consistent multi-view structure. We provide a comprehensive review of breast anatomy, imaging characteristics, and public mammography datasets to systematically identify key sources of bias. Applying MammoClean to three heterogeneous datasets (CBIS-DDSM, TOMPEI-CMMD, VinDr-Mammo), we quantify substantial distributional shifts in breast density and abnormality prevalence. Critically, we demonstrate the direct impact of data corruption: AI models trained on corrupted datasets exhibit significant performance degradation compared to their curated counterparts. By using MammoClean to identify and mitigate bias sources, researchers can construct unified multi-dataset training corpora that enable development of robust models with superior cross-domain generalization. MammoClean provides an essential, reproducible pipeline for bias-aware AI development in mammography, facilitating fairer comparisons and advancing the creation of safe, effective systems that perform equitably across diverse patient populations and clinical settings. The open-source code is publicly available from: https://github.com/Minds-R-Lab/MammoClean.
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            <a href="https://www.alphaxiv.org/abs/2511.02397v1" target="_blank" rel="noopener noreferrer">
                一种基于分组的新型混合色彩校正算法用于彩色点云
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            A Novel Grouping-Based Hybrid Color Correction Algorithm for Color Point Clouds
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kuo-Liang Chung, Ting-Chung Tang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于彩色点云的色彩校正算法，属于计算机视觉中的3D视觉处理范畴。虽然点云技术在某些场景下可能用于商品3D建模，但该论文没有展示与推荐系统、搜索或广告排名的直接关联，也不涉及LLM、Transformer架构或异构数据统一建模等核心关注领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 09:23:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02397v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02397v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Color consistency correction for color point clouds is a fundamental yet important task in 3D rendering and compression applications. In the past, most previous color correction methods aimed at correcting color for color images. The purpose of this paper is to propose a grouping-based hybrid color correction algorithm for color point clouds. Our algorithm begins by estimating the overlapping rate between the aligned source and target point clouds, and then adaptively partitions the target points into two groups, namely the close proximity group Gcl and the moderate proximity group Gmod, or three groups, namely Gcl, Gmod, and the distant proximity group Gdist, when the estimated overlapping rate is low or high, respectively. To correct color for target points in Gcl, a K-nearest neighbors based bilateral interpolation (KBI) method is proposed. To correct color for target points in Gmod, a joint KBI and the histogram equalization (JKHE) method is proposed. For target points in Gdist, a histogram equalization (HE) method is proposed for color correction. Finally, we discuss the grouping-effect free property and the ablation study in our algorithm. The desired color consistency correction benefit of our algorithm has been justified through 1086 testing color point cloud pairs against the state-of-the-art methods. The C++ source code of our algorithm can be accessed from the website: https://github.com/ivpml84079/Point-cloud-color-correction.
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            <a href="https://www.alphaxiv.org/abs/2511.02384v1" target="_blank" rel="noopener noreferrer">
                RxnCaption：将反应图解析重新表述为视觉提示引导的标题生成
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            RxnCaption: Reformulating Reaction Diagram Parsing as Visual Prompt Guided Captioning
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiahe Song, Chuang Wang, Bowen Jiang, Yinfan Wang, Hao Zheng, Xingjian Wei, Chen...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于化学反应图的解析和标题生成，属于化学领域的视觉-语言建模应用。虽然涉及视觉提示和多模态建模，但这是纯粹的化学领域应用，与推荐系统、搜索或广告没有任何潜在关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 09:08:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02384v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02384v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Large-scale chemical reaction datasets are crucial for AI research in chemistry. However, existing chemical reaction data often exist as images within papers, making them not machine-readable and unusable for training machine learning models. In response to this challenge, we propose the RxnCaption framework for the task of chemical Reaction Diagram Parsing (RxnDP). Our framework reformulates the traditional coordinate prediction driven parsing process into an image captioning problem, which Large Vision-Language Models (LVLMs) handle naturally. We introduce a strategy termed "BBox and Index as Visual Prompt" (BIVP), which uses our state-of-the-art molecular detector, MolYOLO, to pre-draw molecular bounding boxes and indices directly onto the input image. This turns the downstream parsing into a natural-language description problem. Extensive experiments show that the BIVP strategy significantly improves structural extraction quality while simplifying model design. We further construct the RxnCaption-11k dataset, an order of magnitude larger than prior real-world literature benchmarks, with a balanced test subset across four layout archetypes. Experiments demonstrate that RxnCaption-VL achieves state-of-the-art performance on multiple metrics. We believe our method, dataset, and models will advance structured information extraction from chemical literature and catalyze broader AI applications in chemistry. We will release data, models, and code on GitHub.
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                M3PD数据集：在实验室和临床环境中使用智能手机前后置摄像头的双视角光电容积描记法（PPG）
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            M3PD Dataset: Dual-view Photoplethysmography (PPG) Using Front-and-rear Cameras of Smartphones in Lab and Clinical Settings
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiankai Tang, Tao Zhang, Jia Li, Yiru Zhang, Mingyu Zhang, Kegang Wang, Yuming H...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医疗领域的生理信号采集（PPG）和智能手机摄像头应用，与推荐系统、搜索或广告的核心技术完全无关。论文内容涉及医疗数据收集和临床设置，属于明确的医疗领域应用，完全落在用户指定的无关主题范围内。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 08:13:11
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                <a href="https://arxiv.org/abs/2511.02349v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02349v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Portable physiological monitoring is essential for early detection and management of cardiovascular disease, but current methods often require specialized equipment that limits accessibility or impose impractical postures that patients cannot maintain. Video-based photoplethysmography on smartphones offers a convenient noninvasive alternative, yet it still faces reliability challenges caused by motion artifacts, lighting variations, and single-view constraints. Few studies have demonstrated reliable application to cardiovascular patients, and no widely used open datasets exist for cross-device accuracy. To address these limitations, we introduce the M3PD dataset, the first publicly available dual-view mobile photoplethysmography dataset, comprising synchronized facial and fingertip videos captured simultaneously via front and rear smartphone cameras from 60 participants (including 47 cardiovascular patients). Building on this dual-view setting, we further propose F3Mamba, which fuses the facial and fingertip views through Mamba-based temporal modeling. The model reduces heart-rate error by 21.9 to 30.2 percent over existing single-view baselines while improving robustness in challenging real-world scenarios. Data and code: https://github.com/Health-HCI-Group/F3Mamba.
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            <a href="https://www.alphaxiv.org/abs/2511.02329v1" target="_blank" rel="noopener noreferrer">
                Cycle-Sync：通过增强的循环一致性同步实现鲁棒的全局相机位姿估计
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            Cycle-Sync: Robust Global Camera Pose Estimation through Enhanced Cycle-Consistent Synchronization
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shaohan Li, Yunpeng Shi, Gilad Lerman
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的相机位姿估计问题，属于纯粹的视觉技术领域。虽然标题提到'同步'和'鲁棒性'，但核心内容是相机位姿估计，这与推荐系统、搜索或广告没有直接关联，也不涉及LLM、Transformer架构或异构数据处理。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 07:31:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02329v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02329v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.NA</span><span class="category-tag">cs.RO</span><span class="category-tag">math.NA</span><span class="category-tag">stat.ME</span><span class="category-tag">90C26</span><span class="category-tag">90C17</span><span class="category-tag">68Q87</span><span class="category-tag">65C20</span><span class="category-tag">90-08</span><span class="category-tag">60-08</span><span class="category-tag">G.1.6; I.4.0</span></div>
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                    We introduce Cycle-Sync, a robust and global framework for estimating camera poses (both rotations and locations). Our core innovation is a location solver that adapts message-passing least squares (MPLS) -- originally developed for group synchronization -- to camera location estimation. We modify MPLS to emphasize cycle-consistent information, redefine cycle consistencies using estimated distances from previous iterations, and incorporate a Welsch-type robust loss. We establish the strongest known deterministic exact-recovery guarantee for camera location estimation, showing that cycle consistency alone -- without access to inter-camera distances -- suffices to achieve the lowest sample complexity currently known. To further enhance robustness, we introduce a plug-and-play outlier rejection module inspired by robust subspace recovery, and we fully integrate cycle consistency into MPLS for rotation synchronization. Our global approach avoids the need for bundle adjustment. Experiments on synthetic and real datasets show that Cycle-Sync consistently outperforms leading pose estimators, including full structure-from-motion pipelines with bundle adjustment.
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            <a href="https://www.alphaxiv.org/abs/2511.02293v1" target="_blank" rel="noopener noreferrer">
                面向边缘设备分割计算的3D点云目标检测
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            3D Point Cloud Object Detection on Edge Devices for Split Computing
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Taisuke Noguchi, Takuya Azumi
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D点云目标检测和边缘计算分割技术，属于纯粹的计算机视觉和边缘计算领域。虽然边缘设备优化在技术上有一定通用性，但论文的3D点云检测核心内容与推荐系统、搜索或广告的异构数据处理没有直接关联，也不涉及Transformer架构或LLM技术的应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 06:15:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02293v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02293v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.DC</span><span class="category-tag">cs.CV</span><span class="category-tag">C.2.4; I.2.10</span></div>
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                    The field of autonomous driving technology is rapidly advancing, with deep learning being a key component. Particularly in the field of sensing, 3D point cloud data collected by LiDAR is utilized to run deep neural network models for 3D object detection. However, these state-of-the-art models are complex, leading to longer processing times and increased power consumption on edge devices. The objective of this study is to address these issues by leveraging Split Computing, a distributed machine learning inference method. Split Computing aims to lessen the computational burden on edge devices, thereby reducing processing time and power consumption. Furthermore, it minimizes the risk of data breaches by only transmitting intermediate data from the deep neural network model. Experimental results show that splitting after voxelization reduces the inference time by 70.8% and the edge device execution time by 90.0%. When splitting within the network, the inference time is reduced by up to 57.1%, and the edge device execution time is reduced by up to 69.5%.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.02277v1" target="_blank" rel="noopener noreferrer">
                欧拉角是否是一种用于基于归一化流的姿态估计的有效旋转参数化方法？
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Are Euler angles a useful rotation parameterisation for pose estimation with Normalizing Flows?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Giorgos Sfikas, Konstantina Nikolaidou, Foteini Papadopoulou, George Retsinas, A...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文主要关注计算机视觉中的姿态估计问题，使用归一化流进行旋转参数化。这属于纯粹的视觉/3D视觉领域，与推荐系统、搜索或广告的核心技术没有直接关联。归一化流技术本身虽然是一种生成模型，但论文的应用场景和核心问题与我的关注领域相距甚远。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 05:28:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02277v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02277v1
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                    Object pose estimation is a task that is of central importance in 3D Computer Vision. Given a target image and a canonical pose, a single point estimate may very often be sufficient; however, a probabilistic pose output is related to a number of benefits when pose is not unambiguous due to sensor and projection constraints or inherent object symmetries. With this paper, we explore the usefulness of using the well-known Euler angles parameterisation as a basis for a Normalizing Flows model for pose estimation. Isomorphic to spatial rotation, 3D pose has been parameterized in a number of ways, either in or out of the context of parameter estimation. We explore the idea that Euler angles, despite their shortcomings, may lead to useful models in a number of aspects, compared to a model built on a more complex parameterisation.
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            <a href="https://www.alphaxiv.org/abs/2511.02271v1" target="_blank" rel="noopener noreferrer">
                医疗报告生成：基于分层任务结构的跨模态因果干预框架
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            Medical Report Generation: A Hierarchical Task Structure-Based Cross-Modal Causal Intervention Framework
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yucheng Song, Yifan Ge, Junhao Li, Zhining Liao, Zhifang Liao
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医疗领域的报告生成，属于医疗领域的特定应用，与推荐系统、搜索或广告的核心技术无关。虽然涉及跨模态建模，但其医疗报告生成的应用场景与我的关注领域没有直接关联，且属于明确的无关主题范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 05:24:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02271v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02271v1
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                    Medical Report Generation (MRG) is a key part of modern medical diagnostics, as it automatically generates reports from radiological images to reduce radiologists' burden. However, reliable MRG models for lesion description face three main challenges: insufficient domain knowledge understanding, poor text-visual entity embedding alignment, and spurious correlations from cross-modal biases. Previous work only addresses single challenges, while this paper tackles all three via a novel hierarchical task decomposition approach, proposing the HTSC-CIF framework. HTSC-CIF classifies the three challenges into low-, mid-, and high-level tasks: 1) Low-level: align medical entity features with spatial locations to enhance domain knowledge for visual encoders; 2) Mid-level: use Prefix Language Modeling (text) and Masked Image Modeling (images) to boost cross-modal alignment via mutual guidance; 3) High-level: a cross-modal causal intervention module (via front-door intervention) to reduce confounders and improve interpretability. Extensive experiments confirm HTSC-CIF's effectiveness, significantly outperforming state-of-the-art (SOTA) MRG methods. Code will be made public upon paper acceptance.
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            <a href="https://www.alphaxiv.org/abs/2511.02247v1" target="_blank" rel="noopener noreferrer">
                基于域不变特征学习和潜在一致性的内窥镜单目绝对深度估计
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            Monocular absolute depth estimation from endoscopy via domain-invariant feature learning and latent consistency
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hao Li, Daiwei Lu, Jesse d'Almeida, Dilara Isik, Ehsan Khodapanah Aghdam, Nick D...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学内窥镜领域的深度估计，属于医疗影像处理的特定应用。虽然涉及计算机视觉技术，但缺乏与推荐系统、搜索或广告领域的直接关联或潜在应用场景。论文的技术内容主要针对医疗诊断工具开发，属于明确的无关主题范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 04:25:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02247v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02247v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Monocular depth estimation (MDE) is a critical task to guide autonomous medical robots. However, obtaining absolute (metric) depth from an endoscopy camera in surgical scenes is difficult, which limits supervised learning of depth on real endoscopic images. Current image-level unsupervised domain adaptation methods translate synthetic images with known depth maps into the style of real endoscopic frames and train depth networks using these translated images with their corresponding depth maps. However a domain gap often remains between real and translated synthetic images. In this paper, we present a latent feature alignment method to improve absolute depth estimation by reducing this domain gap in the context of endoscopic videos of the central airway. Our methods are agnostic to the image translation process and focus on the depth estimation itself. Specifically, the depth network takes translated synthetic and real endoscopic frames as input and learns latent domain-invariant features via adversarial learning and directional feature consistency. The evaluation is conducted on endoscopic videos of central airway phantoms with manually aligned absolute depth maps. Compared to state-of-the-art MDE methods, our approach achieves superior performance on both absolute and relative depth metrics, and consistently improves results across various backbones and pretrained weights. Our code is available at https://github.com/MedICL-VU/MDE.
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            <a href="https://www.alphaxiv.org/abs/2511.02228v1" target="_blank" rel="noopener noreferrer">
                基于协作注意力与一致性引导融合的MRI和PET阿尔茨海默病诊断
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            Collaborative Attention and Consistent-Guided Fusion of MRI and PET for Alzheimer's Disease Diagnosis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Delin Ma, Menghui Zhou, Jun Qi, Yun Yang, Po Yang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像（MRI和PET）在阿尔茨海默病诊断中的应用，属于明确的医疗领域应用。虽然提到了注意力机制和多模态融合技术，但这些技术完全应用于特定医疗诊断场景，与推荐系统、搜索或广告领域没有任何关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 03:42:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02228v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02228v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Alzheimer's disease (AD) is the most prevalent form of dementia, and its early diagnosis is essential for slowing disease progression. Recent studies on multimodal neuroimaging fusion using MRI and PET have achieved promising results by integrating multi-scale complementary features. However, most existing approaches primarily emphasize cross-modal complementarity while overlooking the diagnostic importance of modality-specific features. In addition, the inherent distributional differences between modalities often lead to biased and noisy representations, degrading classification performance. To address these challenges, we propose a Collaborative Attention and Consistent-Guided Fusion framework for MRI and PET based AD diagnosis. The proposed model introduces a learnable parameter representation (LPR) block to compensate for missing modality information, followed by a shared encoder and modality-independent encoders to preserve both shared and specific representations. Furthermore, a consistency-guided mechanism is employed to explicitly align the latent distributions across modalities. Experimental results on the ADNI dataset demonstrate that our method achieves superior diagnostic performance compared with existing fusion strategies.
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            <a href="https://www.alphaxiv.org/abs/2511.02212v1" target="_blank" rel="noopener noreferrer">
                基于残差特征网络和视觉Transformer的高分辨率磁粒子成像系统矩阵恢复
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            High-Resolution Magnetic Particle Imaging System Matrix Recovery Using a Vision Transformer with Residual Feature Network
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Abuobaida M. Khair, Wenjing Jiang, Yousuf Babiker M. Osman, Wenjun Xia, Xiaopeng...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学成像领域的磁粒子成像系统，属于医疗/生物医学应用范畴，与推荐系统、搜索或广告完全无关。论文中提到的视觉Transformer虽然是一种Transformer架构，但应用场景仅限于医疗成像处理，没有展示在推荐系统、搜索或广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 03:03:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02212v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02212v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">physics.med-ph</span><span class="category-tag">cs.CV</span><span class="category-tag">eess.IV</span></div>
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                    This study presents a hybrid deep learning framework, the Vision Transformer with Residual Feature Network (VRF-Net), for recovering high-resolution system matrices in Magnetic Particle Imaging (MPI). MPI resolution often suffers from downsampling and coil sensitivity variations. VRF-Net addresses these challenges by combining transformer-based global attention with residual convolutional refinement, enabling recovery of both large-scale structures and fine details. To reflect realistic MPI conditions, the system matrix is degraded using a dual-stage downsampling strategy. Training employed paired-image super-resolution on the public Open MPI dataset and a simulated dataset incorporating variable coil sensitivity profiles. For system matrix recovery on the Open MPI dataset, VRF-Net achieved nRMSE = 0.403, pSNR = 39.08 dB, and SSIM = 0.835 at 2x scaling, and maintained strong performance even at challenging scale 8x (pSNR = 31.06 dB, SSIM = 0.717). For the simulated dataset, VRF-Net achieved nRMSE = 4.44, pSNR = 28.52 dB, and SSIM = 0.771 at 2x scaling, with stable performance at higher scales. On average, it reduced nRMSE by 88.2%, increased pSNR by 44.7%, and improved SSIM by 34.3% over interpolation and CNN-based methods. In image reconstruction of Open MPI phantoms, VRF-Net further reduced reconstruction error to nRMSE = 1.79 at 2x scaling, while preserving structural fidelity (pSNR = 41.58 dB, SSIM = 0.960), outperforming existing methods. These findings demonstrate that VRF-Net enables sharper, artifact-free system matrix recovery and robust image reconstruction across multiple scales, offering a promising direction for future in vivo applications.
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                基于深度学习的经食管超声心动图节段纵向应变估计
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            Estimation of Segmental Longitudinal Strain in Transesophageal Echocardiography by Deep Learning
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Anders Austlid Taskén, Thierry Judge, Erik Andreas Rye Berg, Jinyang Yu, Bjørnar...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像分析领域，具体涉及超声心动图和心脏应变估计，这属于明确的医学应用范畴。根据用户指定的无关主题列表，医学、生物学等特定领域应用应被排除，且该技术没有明显的推荐系统、搜索或广告应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 03:02:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02210v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02210v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">eess.IV</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Segmental longitudinal strain (SLS) of the left ventricle (LV) is an important prognostic indicator for evaluating regional LV dysfunction, in particular for diagnosing and managing myocardial ischemia. Current techniques for strain estimation require significant manual intervention and expertise, limiting their efficiency and making them too resource-intensive for monitoring purposes. This study introduces the first automated pipeline, autoStrain, for SLS estimation in transesophageal echocardiography (TEE) using deep learning (DL) methods for motion estimation. We present a comparative analysis of two DL approaches: TeeFlow, based on the RAFT optical flow model for dense frame-to-frame predictions, and TeeTracker, based on the CoTracker point trajectory model for sparse long-sequence predictions. As ground truth motion data from real echocardiographic sequences are hardly accessible, we took advantage of a unique simulation pipeline (SIMUS) to generate a highly realistic synthetic TEE (synTEE) dataset of 80 patients with ground truth myocardial motion to train and evaluate both models. Our evaluation shows that TeeTracker outperforms TeeFlow in accuracy, achieving a mean distance error in motion estimation of 0.65 mm on a synTEE test dataset. Clinical validation on 16 patients further demonstrated that SLS estimation with our autoStrain pipeline aligned with clinical references, achieving a mean difference (95\% limits of agreement) of 1.09% (-8.90% to 11.09%). Incorporation of simulated ischemia in the synTEE data improved the accuracy of the models in quantifying abnormal deformation. Our findings indicate that integrating AI-driven motion estimation with TEE can significantly enhance the precision and efficiency of cardiac function assessment in clinical settings.
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            <a href="https://www.alphaxiv.org/abs/2511.02207v1" target="_blank" rel="noopener noreferrer">
                面向草莓植株重建与表型分析的物体中心3D高斯泼溅技术
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            Object-Centric 3D Gaussian Splatting for Strawberry Plant Reconstruction and Phenotyping
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiajia Li, Keyi Zhu, Qianwen Zhang, Dong Chen, Qi Sun, Zhaojian Li
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于农业领域的3D重建和植物表型分析，属于计算机视觉在特定领域（农业）的应用。虽然涉及3D重建技术，但没有任何与推荐系统、搜索或广告相关的潜在应用，完全超出了用户关注的核心技术领域范围。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 02:55:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02207v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02207v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Strawberries are among the most economically significant fruits in the United States, generating over $2 billion in annual farm-gate sales and accounting for approximately 13% of the total fruit production value. Plant phenotyping plays a vital role in selecting superior cultivars by characterizing plant traits such as morphology, canopy structure, and growth dynamics. However, traditional plant phenotyping methods are time-consuming, labor-intensive, and often destructive. Recently, neural rendering techniques, notably Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have emerged as powerful frameworks for high-fidelity 3D reconstruction. By capturing a sequence of multi-view images or videos around a target plant, these methods enable non-destructive reconstruction of complex plant architectures. Despite their promise, most current applications of 3DGS in agricultural domains reconstruct the entire scene, including background elements, which introduces noise, increases computational costs, and complicates downstream trait analysis. To address this limitation, we propose a novel object-centric 3D reconstruction framework incorporating a preprocessing pipeline that leverages the Segment Anything Model v2 (SAM-2) and alpha channel background masking to achieve clean strawberry plant reconstructions. This approach produces more accurate geometric representations while substantially reducing computational time. With a background-free reconstruction, our algorithm can automatically estimate important plant traits, such as plant height and canopy width, using DBSCAN clustering and Principal Component Analysis (PCA). Experimental results show that our method outperforms conventional pipelines in both accuracy and efficiency, offering a scalable and non-destructive solution for strawberry plant phenotyping.
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            <a href="https://www.alphaxiv.org/abs/2511.02206v1" target="_blank" rel="noopener noreferrer">
                基于MRI和血液生物标志物的PET合成语言增强生成建模
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            Language-Enhanced Generative Modeling for PET Synthesis from MRI and Blood Biomarkers
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhengjie Zhang, Xiaoxie Mao, Qihao Guo, Shaoting Zhang, Qi Huang, Mu Zhou, Fang ...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像生成（PET合成），属于医学/生物学特定领域应用，与推荐系统、搜索或广告完全无关。论文标题中提到的语言增强技术仅应用于医疗数据生成，没有展示在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 02:53:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02206v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02206v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Background: Alzheimer's disease (AD) diagnosis heavily relies on amyloid-beta positron emission tomography (Abeta-PET), which is limited by high cost and limited accessibility. This study explores whether Abeta-PET spatial patterns can be predicted from blood-based biomarkers (BBMs) and MRI scans. Methods: We collected Abeta-PET images, T1-weighted MRI scans, and BBMs from 566 participants. A language-enhanced generative model, driven by a large language model (LLM) and multimodal information fusion, was developed to synthesize PET images. Synthesized images were evaluated for image quality, diagnostic consistency, and clinical applicability within a fully automated diagnostic pipeline. Findings: The synthetic PET images closely resemble real PET scans in both structural details (SSIM = 0.920 +/- 0.003) and regional patterns (Pearson's r = 0.955 +/- 0.007). Diagnostic outcomes using synthetic PET show high agreement with real PET-based diagnoses (accuracy = 0.80). Using synthetic PET, we developed a fully automatic AD diagnostic pipeline integrating PET synthesis and classification. The synthetic PET-based model (AUC = 0.78) outperforms T1-based (AUC = 0.68) and BBM-based (AUC = 0.73) models, while combining synthetic PET and BBMs further improved performance (AUC = 0.79). Ablation analysis supports the advantages of LLM integration and prompt engineering. Interpretation: Our language-enhanced generative model synthesizes realistic PET images, enhancing the utility of MRI and BBMs for Abeta spatial pattern assessment and improving the diagnostic workflow for Alzheimer's disease.
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            <a href="https://www.alphaxiv.org/abs/2511.02193v1" target="_blank" rel="noopener noreferrer">
                MM-UNet：用于视网膜血管分割的形态Mamba U形卷积网络
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            MM-UNet: Morph Mamba U-shaped Convolutional Networks for Retinal Vessel Segmentation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiawen Liu, Yuanbo Zeng, Jiaming Liang, Yizhen Yang, Yiheng Zhang, Enhui Cai, Xi...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学图像分割领域，特别是视网膜血管分割，这属于明确的无关主题（医学领域应用）。论文标题中提到的Mamba架构虽然是一种序列建模技术，但应用场景与推荐系统、搜索或广告完全无关，且没有显示出任何在推荐/搜索/广告领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 02:18:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02193v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02193v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Accurate detection of retinal vessels plays a critical role in reflecting a wide range of health status indicators in the clinical diagnosis of ocular diseases. Recently, advances in deep learning have led to a surge in retinal vessel segmentation methods, which have significantly contributed to the quantitative analysis of vascular morphology. However, retinal vasculature differs significantly from conventional segmentation targets in that it consists of extremely thin and branching structures, whose global morphology varies greatly across images. These characteristics continue to pose challenges to segmentation precision and robustness. To address these issues, we propose MM-UNet, a novel architecture tailored for efficient retinal vessel segmentation. The model incorporates Morph Mamba Convolution layers, which replace pointwise convolutions to enhance branching topological perception through morph, state-aware feature sampling. Additionally, Reverse Selective State Guidance modules integrate reverse guidance theory with state-space modeling to improve geometric boundary awareness and decoding efficiency. Extensive experiments conducted on two public retinal vessel segmentation datasets demonstrate the superior performance of the proposed method in segmentation accuracy. Compared to the existing approaches, MM-UNet achieves F1-score gains of 1.64 $\%$ on DRIVE and 1.25 $\%$ on STARE, demonstrating its effectiveness and advancement. The project code is public via https://github.com/liujiawen-jpg/MM-UNet.
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            <a href="https://www.alphaxiv.org/abs/2511.02180v1" target="_blank" rel="noopener noreferrer">
                事件相机自动偏置以减轻闪烁效应
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            Autobiasing Event Cameras for Flickering Mitigation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mehdi Sefidgar Dilmaghani, Waseem Shariff, Cian Ryan, Joe Lemley, Peter Corcoran
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于事件相机的硬件偏置技术来解决闪烁问题，属于纯粹的计算机视觉领域。事件相机是专门用于高速运动捕捉的传感器技术，与推荐系统、搜索或广告没有任何直接或间接的应用关联。该技术改进仅限于视觉传感器层面，无法应用于文本、序列或异构数据的建模。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 01:49:08
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                <a href="https://arxiv.org/abs/2511.02180v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02180v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Understanding and mitigating flicker effects caused by rapid variations in light intensity is critical for enhancing the performance of event cameras in diverse environments. This paper introduces an innovative autonomous mechanism for tuning the biases of event cameras, effectively addressing flicker across a wide frequency range -25 Hz to 500 Hz. Unlike traditional methods that rely on additional hardware or software for flicker filtering, our approach leverages the event cameras inherent bias settings. Utilizing a simple Convolutional Neural Networks -CNNs, the system identifies instances of flicker in a spatial space and dynamically adjusts specific biases to minimize its impact. The efficacy of this autobiasing system was robustly tested using a face detector framework under both well-lit and low-light conditions, as well as across various frequencies. The results demonstrated significant improvements: enhanced YOLO confidence metrics for face detection, and an increased percentage of frames capturing detected faces. Moreover, the average gradient, which serves as an indicator of flicker presence through edge detection, decreased by 38.2 percent in well-lit conditions and by 53.6 percent in low-light conditions. These findings underscore the potential of our approach to significantly improve the functionality of event cameras in a range of adverse lighting scenarios.
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            <a href="https://www.alphaxiv.org/abs/2511.02144v1" target="_blank" rel="noopener noreferrer">
                基于级联主成分分析的快速路面裂缝宽度测量
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            Fast Measuring Pavement Crack Width by Cascading Principal Component Analysis
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhicheng Wang, Junbiao Pang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉中的路面裂缝检测和测量，属于纯粹的视觉应用领域。该方法在推荐系统、搜索或广告领域没有明显的应用潜力，因为它处理的是物理基础设施的视觉检测问题，与用户行为建模、内容排序或广告投放无关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 00:29:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02144v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02144v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">stat.AP</span></div>
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                    Accurate quantification of pavement crack width plays a pivotal role in assessing structural integrity and guiding maintenance interventions. However, achieving precise crack width measurements presents significant challenges due to: (1) the complex, non-uniform morphology of crack boundaries, which limits the efficacy of conventional approaches, and (2) the demand for rapid measurement capabilities from arbitrary pixel locations to facilitate comprehensive pavement condition evaluation. To overcome these limitations, this study introduces a cascaded framework integrating Principal Component Analysis (PCA) and Robust PCA (RPCA) for efficient crack width extraction from digital images. The proposed methodology comprises three sequential stages: (1) initial crack segmentation using established detection algorithms to generate a binary representation, (2) determination of the primary orientation axis for quasi-parallel cracks through PCA, and (3) extraction of the Main Propagation Axis (MPA) for irregular crack geometries using RPCA. Comprehensive evaluations were conducted across three publicly available datasets, demonstrating that the proposed approach achieves superior performance in both computational efficiency and measurement accuracy compared to existing state-of-the-art techniques.
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                从实例分割到浮游有孔虫三维生长轨迹重建
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            From Instance Segmentation to 3D Growth Trajectory Reconstruction in Planktonic Foraminifera
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Huahua Lin, Xiaohao Cai, Mark Nixon, James M. Mulqueeney, Thomas H. G. Ezard
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于浮游有孔虫的3D生长轨迹重建，属于生物学和计算机视觉的交叉领域。这与我的关注点完全无关，因为浮游有孔虫是生物学特定研究对象，3D重建技术在此背景下没有明显的推荐系统、搜索或广告应用潜力。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-04 00:26:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.02142v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.02142v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Planktonic foraminifera, marine protists characterized by their intricate chambered shells, serve as valuable indicators of past and present environmental conditions. Understanding their chamber growth trajectory provides crucial insights into organismal development and ecological adaptation under changing environments. However, automated tracing of chamber growth from imaging data remains largely unexplored, with existing approaches relying heavily on manual segmentation of each chamber, which is time-consuming and subjective. In this study, we propose an end-to-end pipeline that integrates instance segmentation, a computer vision technique not extensively explored in foraminifera, with a dedicated chamber ordering algorithm to automatically reconstruct three-dimensional growth trajectories from high-resolution computed tomography scans. We quantitatively and qualitatively evaluate multiple instance segmentation methods, each optimized for distinct spatial features of the chambers, and examine their downstream influence on growth-order reconstruction accuracy. Experimental results on expert-annotated datasets demonstrate that the proposed pipeline substantially reduces manual effort while maintaining biologically meaningful accuracy. Although segmentation models exhibit under-segmentation in smaller chambers due to reduced voxel fidelity and subtle inter-chamber connectivity, the chamber-ordering algorithm remains robust, achieving consistent reconstruction of developmental trajectories even under partial segmentation. This work provides the first fully automated and reproducible pipeline for digital foraminiferal growth analysis, establishing a foundation for large-scale, data-driven ecological studies.
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